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['Joaquin Vanschoren', 'Mikio L. Braun', 'Cheng Soon Ong']

1402.6013v1

We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application. They go beyond the more traditional repositories for data sets and software packages in that they allow researchers to also easily share the results they obtained in experiments and to compare their solutions with those of others.

Open science in machine learning

http://arxiv.org/pdf/1402.6013v1

Title Open science machine learning Summary present OpenML mldata open science platform provides easy access machine learning data software result encourage study application go beyond traditional repository data set software package allow researcher also easily share result obtained experiment compare solution others Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Aharon Ben-Tal', 'Elad Hazan', 'Tomer Koren', 'Shie Mannor']

1402.6361v1

Robust optimization is a common framework in optimization under uncertainty when the problem parameters are not known, but it is rather known that the parameters belong to some given uncertainty set. In the robust optimization framework the problem solved is a min-max problem where a solution is judged according to its performance on the worst possible realization of the parameters. In many cases, a straightforward solution of the robust optimization problem of a certain type requires solving an optimization problem of a more complicated type, and in some cases even NP-hard. For example, solving a robust conic quadratic program, such as those arising in robust SVM, ellipsoidal uncertainty leads in general to a semidefinite program. In this paper we develop a method for approximately solving a robust optimization problem using tools from online convex optimization, where in every stage a standard (non-robust) optimization program is solved. Our algorithms find an approximate robust solution using a number of calls to an oracle that solves the original (non-robust) problem that is inversely proportional to the square of the target accuracy.

Oracle-Based Robust Optimization via Online Learning

http://arxiv.org/pdf/1402.6361v1

Title OracleBased Robust Optimization via Online Learning Summary Robust optimization common framework optimization uncertainty problem parameter known rather known parameter belong given uncertainty set robust optimization framework problem solved minmax problem solution judged according performance worst possible realization parameter many case straightforward solution robust optimization problem certain type requires solving optimization problem complicated type case even NPhard example solving robust conic quadratic program arising robust SVM ellipsoidal uncertainty lead general semidefinite program paper develop method approximately solving robust optimization problem using tool online convex optimization every stage standard nonrobust optimization program solved algorithm find approximate robust solution using number call oracle solves original nonrobust problem inversely proportional square target accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['M. H. Marghny', 'Ahmed I. Taloba']

1402.6859v1

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simultaneously. The algorithmimprovesthe estimation of centroids of the generative distribution during the process of clustering and outlier discovery. The proposed algorithm consists of two stages. The first stage consists of improved genetic k-means algorithm (IGK) process, while the second stage iteratively removes the vectors which are far from their cluster centroids.

Outlier Detection using Improved Genetic K-means

http://arxiv.org/pdf/1402.6859v1

Title Outlier Detection using Improved Genetic Kmeans Summary outlier detection problem case similar classification problem example main concern clusteringbased outlier detection algorithm find cluster outlier often regarded noise removed order make reliable clustering article present algorithm provides outlier detection data clustering simultaneously algorithmimprovesthe estimation centroid generative distribution process clustering outlier discovery proposed algorithm consists two stage first stage consists improved genetic kmeans algorithm IGK process second stage iteratively remove vector far cluster centroid Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fabian Pedregosa', 'Michael Eickenberg', 'Philippe Ciuciu', 'Bertrand Thirion', 'Alexandre Gramfort']

1402.7015v6

Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding score in two different datasets. Our results show that the R1-GLM model significantly outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.

Data-driven HRF estimation for encoding and decoding models

http://arxiv.org/pdf/1402.7015v6

Title Datadriven HRF estimation encoding decoding model Summary Despite common usage canonical dataindependent hemodynamic response function HRF known shape HRF varies across brain region subject suggests datadriven estimation function could lead statistical power modeling BOLD fMRI data However unconstrained estimation HRF yield highly unstable result number free parameter large develop method joint estimation activation HRF using rank constraint causing estimated HRF equal across eventsconditions yet permitting different across voxels Model estimation lead optimization problem propose solve efficient quasiNewton method exploiting fast gradient computation model called GLM Rank1 constraint R1GLM extended setting GLM separate design shown improve decoding accuracy brain activity decoding experiment compare 10 different HRF modeling method term encoding decoding score two different datasets result show R1GLM model significantly outperforms competing method encoding decoding setting positioning attractive method point view accuracy computational efficiency Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wei Chen', 'Tian Lin', 'Cheng Yang']

1403.0057v2

Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks (referred collectively as items in this paper) are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods for these topics to avoid redoing influence maximization for each item from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

Real-time Topic-aware Influence Maximization Using Preprocessing

http://arxiv.org/pdf/1403.0057v2

Title Realtime Topicaware Influence Maximization Using Preprocessing Summary Influence maximization task finding set seed node social network influence spread seed node based certain influence diffusion model maximized Topicaware influence diffusion model recently proposed address issue influence pair user often topicdependent information idea innovation etc propagated network referred collectively item paper typically mixture topic paper focus topicaware influence maximization task particular study preprocessing method topic avoid redoing influence maximization item scratch explore two preprocessing algorithm theoretical justification empirical result data obtained couple existing study demonstrate one algorithm stand strong candidate providing microsecond online response time competitive influence spread reasonable preprocessing effort Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tahereh Babaie', 'Sanjay Chawla', 'Sebastien Ardon']

1403.0157v1

In this paper we focus on the detection of network anomalies like Denial of Service (DoS) attacks and port scans in a unified manner. While there has been an extensive amount of research in network anomaly detection, current state of the art methods are only able to detect one class of anomalies at the cost of others. The key tool we will use is based on the spectral decomposition of a trajectory/hankel matrix which is able to detect deviations from both between and within correlation present in the observed network traffic data. Detailed experiments on synthetic and real network traces shows a significant improvement in detection capability over competing approaches. In the process we also address the issue of robustness of anomaly detection systems in a principled fashion.

Network Traffic Decomposition for Anomaly Detection

http://arxiv.org/pdf/1403.0157v1

Title Network Traffic Decomposition Anomaly Detection Summary paper focus detection network anomaly like Denial Service DoS attack port scan unified manner extensive amount research network anomaly detection current state art method able detect one class anomaly cost others key tool use based spectral decomposition trajectoryhankel matrix able detect deviation within correlation present observed network traffic data Detailed experiment synthetic real network trace show significant improvement detection capability competing approach process also address issue robustness anomaly detection system principled fashion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu']

1403.1336v1

This paper exclusively reports the efficiency of AIS-INMACA. AIS-INMACA has created good impact on solving major problems in bioinformatics like protein region identification and promoter region prediction with less time (Pokkuluri Kiran Sree, 2014). This AIS-INMACA is now came with several variations (Pokkuluri Kiran Sree, 2014) towards projecting it as a tool in bioinformatics for solving many problems in bioinformatics. So this paper will be very much useful for so many researchers who are working in the domain of bioinformatics with cellular automata.

An Extensive Repot on the Efficiency of AIS-INMACA (A Novel Integrated MACA based Clonal Classifier for Protein Coding and Promoter Region Prediction)

http://arxiv.org/pdf/1403.1336v1

Title Extensive Repot Efficiency AISINMACA Novel Integrated MACA based Clonal Classifier Protein Coding Promoter Region Prediction Summary paper exclusively report efficiency AISINMACA AISINMACA created good impact solving major problem bioinformatics like protein region identification promoter region prediction le time Pokkuluri Kiran Sree 2014 AISINMACA came several variation Pokkuluri Kiran Sree 2014 towards projecting tool bioinformatics solving many problem bioinformatics paper much useful many researcher working domain bioinformatics cellular automaton Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hanie Sedghi', 'Edmond Jonckheere']

1403.1863v1

Robust control and maintenance of the grid relies on accurate data. Both PMUs and state estimators are prone to false data injection attacks. Thus, it is crucial to have a mechanism for fast and accurate detection of an agent maliciously tampering with the data---for both preventing attacks that may lead to blackouts, and for routine monitoring and control tasks of current and future grids. We propose a decentralized false data injection detection scheme based on Markov graph of the bus phase angles. We utilize the Conditional Covariance Test (CCT) to learn the structure of the grid. Using the DC power flow model, we show that under normal circumstances, and because of walk-summability of the grid graph, the Markov graph of the voltage angles can be determined by the power grid graph. Therefore, a discrepancy between calculated Markov graph and learned structure should trigger the alarm. Local grid topology is available online from the protection system and we exploit it to check for mismatch. Should a mismatch be detected, we use correlation anomaly score to detect the set of attacked nodes. Our method can detect the most recent stealthy deception attack on the power grid that assumes knowledge of bus-branch model of the system and is capable of deceiving the state estimator, damaging power network observatory, control, monitoring, demand response and pricing schemes. Specifically, under the stealthy deception attack, the Markov graph of phase angles changes. In addition to detect a state of attack, our method can detect the set of attacked nodes. To the best of our knowledge, our remedy is the first to comprehensively detect this sophisticated attack and it does not need additional hardware. Moreover, our detection scheme is successful no matter the size of the attacked subset. Simulation of various power networks confirms our claims.

Statistical Structure Learning, Towards a Robust Smart Grid

http://arxiv.org/pdf/1403.1863v1

Title Statistical Structure Learning Towards Robust Smart Grid Summary Robust control maintenance grid relies accurate data PMUs state estimator prone false data injection attack Thus crucial mechanism fast accurate detection agent maliciously tampering datafor preventing attack may lead blackout routine monitoring control task current future grid propose decentralized false data injection detection scheme based Markov graph bus phase angle utilize Conditional Covariance Test CCT learn structure grid Using DC power flow model show normal circumstance walksummability grid graph Markov graph voltage angle determined power grid graph Therefore discrepancy calculated Markov graph learned structure trigger alarm Local grid topology available online protection system exploit check mismatch mismatch detected use correlation anomaly score detect set attacked node method detect recent stealthy deception attack power grid assumes knowledge busbranch model system capable deceiving state estimator damaging power network observatory control monitoring demand response pricing scheme Specifically stealthy deception attack Markov graph phase angle change addition detect state attack method detect set attacked node best knowledge remedy first comprehensively detect sophisticated attack need additional hardware Moreover detection scheme successful matter size attacked subset Simulation various power network confirms claim Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mehdi Naseriparsa', 'Mohammad Mansour Riahi Kashani']

1403.1949v1

Classification algorithms are unable to make reliable models on the datasets with huge sizes. These datasets contain many irrelevant and redundant features that mislead the classifiers. Furthermore, many huge datasets have imbalanced class distribution which leads to bias over majority class in the classification process. In this paper combination of unsupervised dimensionality reduction methods with resampling is proposed and the results are tested on Lung-Cancer dataset. In the first step PCA is applied on Lung-Cancer dataset to compact the dataset and eliminate irrelevant features and in the second step SMOTE resampling is carried out to balance the class distribution and increase the variety of sample domain. Finally, Naive Bayes classifier is applied on the resulting dataset and the results are compared and evaluation metrics are calculated. The experiments show the effectiveness of the proposed method across four evaluation metrics: Overall accuracy, False Positive Rate, Precision, Recall.

Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset

http://arxiv.org/pdf/1403.1949v1

Title Combination PCA SMOTE Resampling Boost Prediction Rate Lung Cancer Dataset Summary Classification algorithm unable make reliable model datasets huge size datasets contain many irrelevant redundant feature mislead classifier Furthermore many huge datasets imbalanced class distribution lead bias majority class classification process paper combination unsupervised dimensionality reduction method resampling proposed result tested LungCancer dataset first step PCA applied LungCancer dataset compact dataset eliminate irrelevant feature second step SMOTE resampling carried balance class distribution increase variety sample domain Finally Naive Bayes classifier applied resulting dataset result compared evaluation metric calculated experiment show effectiveness proposed method across four evaluation metric Overall accuracy False Positive Rate Precision Recall Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Meng Fang', 'Jie Yin', 'Xingquan Zhu']

1403.2484v1

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.

Transfer Learning across Networks for Collective Classification

http://arxiv.org/pdf/1403.2484v1

Title Transfer Learning across Networks Collective Classification Summary paper address problem transferring useful knowledge source network predict node label newly formed target network existing transfer learning research primarily focused vectorbased data instance assumed independent identically distributed effectively transfer knowledge across different information network well studied mainly network may distinct node feature link relationship node paper propose new transfer learning algorithm attempt transfer common latent structure feature across source target network proposed algorithm discovers latent feature constructing label propagation matrix source target network mapping shared latent feature space latent feature capture common structure pattern shared two network serve domainindependent feature transferred network Together domaindependent node feature thereafter propose iterative classification algorithm leverage label correlation predict node label target network Experiments realworld network demonstrate proposed algorithm successfully achieve knowledge transfer network help improve accuracy classifying node target network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yanping Chen', 'Adena Why', 'Gustavo Batista', 'Agenor Mafra-Neto', 'Eamonn Keogh']

1403.2654v1

The ability to use inexpensive, noninvasive sensors to accurately classify flying insects would have significant implications for entomological research, and allow for the development of many useful applications in vector control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts on this task. To date, however, none of this research has had a lasting impact. In this work, we explain this lack of progress. We attribute the stagnation on this problem to several factors, including the use of acoustic sensing devices, the over-reliance on the single feature of wingbeat frequency, and the attempts to learn complex models with relatively little data. In contrast, we show that pseudo-acoustic optical sensors can produce vastly superior data, that we can exploit additional features, both intrinsic and extrinsic to the insect's flight behavior, and that a Bayesian classification approach allows us to efficiently learn classification models that are very robust to over-fitting. We demonstrate our findings with large scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.

Flying Insect Classification with Inexpensive Sensors

http://arxiv.org/pdf/1403.2654v1

Title Flying Insect Classification Inexpensive Sensors Summary ability use inexpensive noninvasive sensor accurately classify flying insect would significant implication entomological research allow development many useful application vector control medical agricultural entomology Given last sixty year seen many research effort task date however none research lasting impact work explain lack progress attribute stagnation problem several factor including use acoustic sensing device overreliance single feature wingbeat frequency attempt learn complex model relatively little data contrast show pseudoacoustic optical sensor produce vastly superior data exploit additional feature intrinsic extrinsic insect flight behavior Bayesian classification approach allows u efficiently learn classification model robust overfitting demonstrate finding large scale experiment dwarf previous work combined measured number insect number specie considered Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Cagdas Bilen', 'Gilles Puy', 'Rémi Gribonval', 'Laurent Daudet']

1403.4267v2

We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing. We propose a new approach for analyzing the trade off between the sparsity and low rank constraints in these approaches which not only helps to provide guidelines to adjust the weights between the aforementioned constraints, but also enables new simulation strategies for evaluating performance. We then provide simulation results for phase retrieval and phase calibration cases both to demonstrate the consistency of the proposed method with other approaches and to evaluate the change of performance with different weights for the sparsity and low rank structure constraints.

Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit

http://arxiv.org/pdf/1403.4267v2

Title Balancing Sparsity Rank Constraints Quadratic Basis Pursuit Summary investigate method simultaneously enforce sparsity lowrank structure matrix often employed sparse phase retrieval problem phase calibration problem compressive sensing propose new approach analyzing trade sparsity low rank constraint approach help provide guideline adjust weight aforementioned constraint also enables new simulation strategy evaluating performance provide simulation result phase retrieval phase calibration case demonstrate consistency proposed method approach evaluate change performance different weight sparsity low rank structure constraint Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Raphael Fonteneau', 'L. A. Prashanth']

1403.4514v2

We propose novel policy search algorithms in the context of off-policy, batch mode reinforcement learning (RL) with continuous state and action spaces. Given a batch collection of trajectories, we perform off-line policy evaluation using an algorithm similar to that by [Fonteneau et al., 2010]. Using this Monte-Carlo like policy evaluator, we perform policy search in a class of parameterized policies. We propose both first order policy gradient and second order policy Newton algorithms. All our algorithms incorporate simultaneous perturbation estimates for the gradient as well as the Hessian of the cost-to-go vector, since the latter is unknown and only biased estimates are available. We demonstrate their practicality on a simple 1-dimensional continuous state space problem.

Simultaneous Perturbation Algorithms for Batch Off-Policy Search

http://arxiv.org/pdf/1403.4514v2

Title Simultaneous Perturbation Algorithms Batch OffPolicy Search Summary propose novel policy search algorithm context offpolicy batch mode reinforcement learning RL continuous state action space Given batch collection trajectory perform offline policy evaluation using algorithm similar Fonteneau et al 2010 Using MonteCarlo like policy evaluator perform policy search class parameterized policy propose first order policy gradient second order policy Newton algorithm algorithm incorporate simultaneous perturbation estimate gradient well Hessian costtogo vector since latter unknown biased estimate available demonstrate practicality simple 1dimensional continuous state space problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jie Xu', 'Mihaela van der Schaar', 'Jiangchuan Liu', 'Haitao Li']

1403.5603v1

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.

Forecasting Popularity of Videos using Social Media

http://arxiv.org/pdf/1403.5603v1

Title Forecasting Popularity Videos using Social Media Summary paper present systematic online prediction method SocialForecast capable accurately forecast popularity video promoted social medium SocialForecast explicitly considers dynamically changing evolving propagation pattern video social medium making popularity forecast thereby situation context aware SocialForecast aim maximize forecast reward defined tradeoff popularity prediction accuracy timeliness prediction issued forecasting performed online requires training phase priori knowledge analytically bound prediction performance loss SocialForecast compared obtained omniscient oracle prove bound sublinear number video arrival thereby guaranteeing shortterm performance well asymptotic convergence optimal performance addition conduct extensive experiment using realworld data trace collected video shared RenRen one largest online social network China experiment show proposed method outperforms existing viewbased approach popularity prediction contextaware 30 term prediction reward Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu']

1403.5933v1

Most of the problems in bioinformatics are now the challenges in computing. This paper aims at building a classifier based on Multiple Attractor Cellular Automata (MACA) which uses fuzzy logic. It is strengthened with an artificial Immune System Technique (AIS), Clonal algorithm for identifying a protein coding and promoter region in a given DNA sequence. The proposed classifier is named as AIS-INMACA introduces a novel concept to combine CA with artificial immune system to produce a better classifier which can address major problems in bioinformatics. This will be the first integrated algorithm which can predict both promoter and protein coding regions. To obtain good fitness rules the basic concept of Clonal selection algorithm was used. The proposed classifier can handle DNA sequences of lengths 54,108,162,252,354. This classifier gives the exact boundaries of both protein and promoter regions with an average accuracy of 89.6%. This classifier was tested with 97,000 data components which were taken from Fickett & Toung, MPromDb, and other sequences from a renowned medical university. This proposed classifier can handle huge data sets and can find protein and promoter regions even in mixed and overlapped DNA sequences. This work also aims at identifying the logicality between the major problems in bioinformatics and tries to obtaining a common frame work for addressing major problems in bioinformatics like protein structure prediction, RNA structure prediction, predicting the splicing pattern of any primary transcript and analysis of information content in DNA, RNA, protein sequences and structure. This work will attract more researchers towards application of CA as a potential pattern classifier to many important problems in bioinformatics

AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for Protein Coding and Promoter Region Prediction

http://arxiv.org/pdf/1403.5933v1

Title AISINMACA Novel Integrated MACA Based Clonal Classifier Protein Coding Promoter Region Prediction Summary problem bioinformatics challenge computing paper aim building classifier based Multiple Attractor Cellular Automata MACA us fuzzy logic strengthened artificial Immune System Technique AIS Clonal algorithm identifying protein coding promoter region given DNA sequence proposed classifier named AISINMACA introduces novel concept combine CA artificial immune system produce better classifier address major problem bioinformatics first integrated algorithm predict promoter protein coding region obtain good fitness rule basic concept Clonal selection algorithm used proposed classifier handle DNA sequence length 54108162252354 classifier give exact boundary protein promoter region average accuracy 896 classifier tested 97000 data component taken Fickett Toung MPromDb sequence renowned medical university proposed classifier handle huge data set find protein promoter region even mixed overlapped DNA sequence work also aim identifying logicality major problem bioinformatics try obtaining common frame work addressing major problem bioinformatics like protein structure prediction RNA structure prediction predicting splicing pattern primary transcript analysis information content DNA RNA protein sequence structure work attract researcher towards application CA potential pattern classifier many important problem bioinformatics Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Bryan Perozzi', 'Rami Al-Rfou', 'Steven Skiena']

1403.6652v2

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.

DeepWalk: Online Learning of Social Representations

http://arxiv.org/pdf/1403.6652v2

Title DeepWalk Online Learning Social Representations Summary present DeepWalk novel approach learning latent representation vertex network latent representation encode social relation continuous vector space easily exploited statistical model DeepWalk generalizes recent advancement language modeling unsupervised feature learning deep learning sequence word graph DeepWalk us local information obtained truncated random walk learn latent representation treating walk equivalent sentence demonstrate DeepWalks latent representation several multilabel network classification task social network BlogCatalog Flickr YouTube result show DeepWalk outperforms challenging baseline allowed global view network especially presence missing information DeepWalks representation provide F1 score 10 higher competing method labeled data sparse experiment DeepWalks representation able outperform baseline method using 60 le training data DeepWalk also scalable online learning algorithm build useful incremental result trivially parallelizable quality make suitable broad class real world application network classification anomaly detection Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xiaomin Lin', 'Peter A. Beling', 'Randy Cogill']

1403.6822v1

We compare the performance of Inverse Reinforcement Learning (IRL) with the relative new model of Multi-agent Inverse Reinforcement Learning (MIRL). Before comparing the methods, we extend a published Bayesian IRL approach that is only applicable to the case where the reward is only state dependent to a general one capable of tackling the case where the reward depends on both state and action. Comparison between IRL and MIRL is made in the context of an abstract soccer game, using both a game model in which the reward depends only on state and one in which it depends on both state and action. Results suggest that the IRL approach performs much worse than the MIRL approach. We speculate that the underperformance of IRL is because it fails to capture equilibrium information in the manner possible in MIRL.

Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example

http://arxiv.org/pdf/1403.6822v1

Title Comparison Multiagent Singleagent Inverse Learning Simulated Soccer Example Summary compare performance Inverse Reinforcement Learning IRL relative new model Multiagent Inverse Reinforcement Learning MIRL comparing method extend published Bayesian IRL approach applicable case reward state dependent general one capable tackling case reward depends state action Comparison IRL MIRL made context abstract soccer game using game model reward depends state one depends state action Results suggest IRL approach performs much worse MIRL approach speculate underperformance IRL fails capture equilibrium information manner possible MIRL Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ayman I. Madbouly', 'Amr M. Gody', 'Tamer M. Barakat']

1403.7726v1

Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer networks. Features extracted from network traffic can be used as sign to detect anomalies. However with the huge amount of network traffic, collected data contains irrelevant and redundant features that affect the detection rate of the IDS, consumes high amount of system resources, and slowdown the training and testing process of the IDS. In this paper, a new feature selection model is proposed; this model can effectively select the most relevant features for intrusion detection. Our goal is to build a lightweight intrusion detection system by using a reduced features set. Deleting irrelevant and redundant features helps to build a faster training and testing process, to have less resource consumption as well as to maintain high detection rates. The effectiveness and the feasibility of our feature selection model were verified by several experiments on KDD intrusion detection dataset. The experimental results strongly showed that our model is not only able to yield high detection rates but also to speed up the detection process.

Relevant Feature Selection Model Using Data Mining for Intrusion Detection System

http://arxiv.org/pdf/1403.7726v1

Title Relevant Feature Selection Model Using Data Mining Intrusion Detection System Summary Network intrusion become significant threat recent year result increased demand computer network critical system Intrusion detection system IDS widely deployed defense measure computer network Features extracted network traffic used sign detect anomaly However huge amount network traffic collected data contains irrelevant redundant feature affect detection rate IDS consumes high amount system resource slowdown training testing process IDS paper new feature selection model proposed model effectively select relevant feature intrusion detection goal build lightweight intrusion detection system using reduced feature set Deleting irrelevant redundant feature help build faster training testing process le resource consumption well maintain high detection rate effectiveness feasibility feature selection model verified several experiment KDD intrusion detection dataset experimental result strongly showed model able yield high detection rate also speed detection process Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Miron B. Kursa', 'Alicja A. Wieczorkowska']

1403.7746v1

In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random ferns, using jazz recordings as input data. Our main result is obtaining much faster classification and higher F-score. We also achieve substantial reduction of the model size.

Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings

http://arxiv.org/pdf/1403.7746v1

Title Multilabel Ferns Efficient Recognition Musical Instruments Recordings Summary paper introduce multilabel fern apply technique automatic classification musical instrument audio recording compare performance proposed method set binary random fern using jazz recording input data main result obtaining much faster classification higher Fscore also achieve substantial reduction model size Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Stratis Ioannidis', 'Andrea Montanari', 'Udi Weinsberg', 'Smriti Bhagat', 'Nadia Fawaz', 'Nina Taft']

1403.8084v1

Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data. Can a privacy-conscious user benefit from personalization while simultaneously protecting her private attributes? We study this question in the context of a rating prediction service based on matrix factorization. We construct a protocol of interactions between the service and users that has remarkable optimality properties: it is privacy-preserving, in that no inference algorithm can succeed in inferring a user's private attribute with a probability better than random guessing; it has maximal accuracy, in that no other privacy-preserving protocol improves rating prediction; and, finally, it involves a minimal disclosure, as the prediction accuracy strictly decreases when the service reveals less information. We extensively evaluate our protocol using several rating datasets, demonstrating that it successfully blocks the inference of gender, age and political affiliation, while incurring less than 5% decrease in the accuracy of rating prediction.

Privacy Tradeoffs in Predictive Analytics

http://arxiv.org/pdf/1403.8084v1

Title Privacy Tradeoffs Predictive Analytics Summary Online service routinely mine user data predict user preference make recommendation place targeted ad Recent research demonstrated several private user attribute political affiliation sexual orientation gender inferred data privacyconscious user benefit personalization simultaneously protecting private attribute study question context rating prediction service based matrix factorization construct protocol interaction service user remarkable optimality property privacypreserving inference algorithm succeed inferring user private attribute probability better random guessing maximal accuracy privacypreserving protocol improves rating prediction finally involves minimal disclosure prediction accuracy strictly decrease service reveals le information extensively evaluate protocol using several rating datasets demonstrating successfully block inference gender age political affiliation incurring le 5 decrease accuracy rating prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ines Marusic', 'James Worrell']

1405.0514v2

We consider the complexity of equivalence and learning for multiplicity tree automata, i.e., weighted tree automata over a field. We first show that the equivalence problem is logspace equivalent to polynomial identity testing, the complexity of which is a longstanding open problem. Secondly, we derive lower bounds on the number of queries needed to learn multiplicity tree automata in Angluin's exact learning model, over both arbitrary and fixed fields. Habrard and Oncina (2006) give an exact learning algorithm for multiplicity tree automata, in which the number of queries is proportional to the size of the target automaton and the size of a largest counterexample, represented as a tree, that is returned by the Teacher. However, the smallest tree-counterexample may be exponential in the size of the target automaton. Thus the above algorithm does not run in time polynomial in the size of the target automaton, and has query complexity exponential in the lower bound. Assuming a Teacher that returns minimal DAG representations of counterexamples, we give a new exact learning algorithm whose query complexity is quadratic in the target automaton size, almost matching the lower bound, and improving the best previously-known algorithm by an exponential factor.

Complexity of Equivalence and Learning for Multiplicity Tree Automata

http://arxiv.org/pdf/1405.0514v2

Title Complexity Equivalence Learning Multiplicity Tree Automata Summary consider complexity equivalence learning multiplicity tree automaton ie weighted tree automaton field first show equivalence problem logspace equivalent polynomial identity testing complexity longstanding open problem Secondly derive lower bound number query needed learn multiplicity tree automaton Angluins exact learning model arbitrary fixed field Habrard Oncina 2006 give exact learning algorithm multiplicity tree automaton number query proportional size target automaton size largest counterexample represented tree returned Teacher However smallest treecounterexample may exponential size target automaton Thus algorithm run time polynomial size target automaton query complexity exponential lower bound Assuming Teacher return minimal DAG representation counterexample give new exact learning algorithm whose query complexity quadratic target automaton size almost matching lower bound improving best previouslyknown algorithm exponential factor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Akhlaqur Rahman', 'Sumaira Tasnim']

1405.1304v1

In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

Application of Machine Learning Techniques in Aquaculture

http://arxiv.org/pdf/1405.1304v1

Title Application Machine Learning Techniques Aquaculture Summary paper present application different machine learning algorithm aquaculture Machine learning algorithm learn model historical data aquaculture historical data obtained farm practice yield environmental data source Associations different variable obtained applying machine learning algorithm historical data paper present application different machine learning algorithm aquaculture application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rafael Izbicki', 'Rafael Bassi Stern']

1405.3292v1

Experts classifying data are often imprecise. Recently, several models have been proposed to train classifiers using the noisy labels generated by these experts. How to choose between these models? In such situations, the true labels are unavailable. Thus, one cannot perform model selection using the standard versions of methods such as empirical risk minimization and cross validation. In order to allow model selection, we present a surrogate loss and provide theoretical guarantees that assure its consistency. Next, we discuss how this loss can be used to tune a penalization which introduces sparsity in the parameters of a traditional class of models. Sparsity provides more parsimonious models and can avoid overfitting. Nevertheless, it has seldom been discussed in the context of noisy labels due to the difficulty in model selection and, therefore, in choosing tuning parameters. We apply these techniques to several sets of simulated and real data.

Learning with many experts: model selection and sparsity

http://arxiv.org/pdf/1405.3292v1

Title Learning many expert model selection sparsity Summary Experts classifying data often imprecise Recently several model proposed train classifier using noisy label generated expert choose model situation true label unavailable Thus one cannot perform model selection using standard version method empirical risk minimization cross validation order allow model selection present surrogate loss provide theoretical guarantee assure consistency Next discus loss used tune penalization introduces sparsity parameter traditional class model Sparsity provides parsimonious model avoid overfitting Nevertheless seldom discussed context noisy label due difficulty model selection therefore choosing tuning parameter apply technique several set simulated real data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tieming Chen', 'Xu Zhang', 'Shichao Jin', 'Okhee Kim']

1405.3410v1

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average.

Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

http://arxiv.org/pdf/1405.3410v1

Title Efficient classification using parallel scalable compressed model application intrusion detection Summary order achieve high efficiency classification intrusion detection compressed model proposed paper combine horizontal compression vertical compression OneR utilized horizontal compression attribute reduction affinity propagation employed vertical compression select small representative exemplar large training data able computationally compress larger volume training data scalability MapReduce based parallelization approach implemented evaluated step model compression process abovementioned common efficient classification method directly used Experimental application study two publicly available datasets intrusion detection KDD99 CMDC2012 demonstrates classification using compressed model proposed effectively speed detection procedure 184 time importantly cost minimal accuracy difference le 1 average Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mohammad Abu Alsheikh', 'Shaowei Lin', 'Dusit Niyato', 'Hwee-Pink Tan']

1405.4463v2

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

http://arxiv.org/pdf/1405.4463v2

Title Machine Learning Wireless Sensor Networks Algorithms Strategies Applications Summary Wireless sensor network monitor dynamic environment change rapidly time dynamic behavior either caused external factor initiated system designer adapt condition sensor network often adopt machine learning technique eliminate need unnecessary redesign Machine learning also inspires many practical solution maximize resource utilization prolong lifespan network paper present extensive literature review period 20022013 machine learning method used address common issue wireless sensor network WSNs advantage disadvantage proposed algorithm evaluated corresponding problem also provide comparative guide aid WSN designer developing suitable machine learning solution specific application challenge Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Everaldo Aguiar', 'Saurabh Nagrecha', 'Nitesh V. Chawla']

1405.5147v1

In the nascent days of e-content delivery, having a superior product was enough to give companies an edge against the competition. With today's fiercely competitive market, one needs to be multiple steps ahead, especially when it comes to understanding consumers. Focusing on a large set of web portals owned and managed by a private communications company, we propose methods by which these sites' clickstream data can be used to provide a deep understanding of their visitors, as well as their interests and preferences. We further expand the use of this data to show that it can be effectively used to predict user engagement to video streams.

Predicting Online Video Engagement Using Clickstreams

http://arxiv.org/pdf/1405.5147v1

Title Predicting Online Video Engagement Using Clickstreams Summary nascent day econtent delivery superior product enough give company edge competition today fiercely competitive market one need multiple step ahead especially come understanding consumer Focusing large set web portal owned managed private communication company propose method site clickstream data used provide deep understanding visitor well interest preference expand use data show effectively used predict user engagement video stream Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Olivier Fercoq', 'Zheng Qu', 'Peter Richtárik', 'Martin Takáč']

1405.5300v2

We propose an efficient distributed randomized coordinate descent method for minimizing regularized non-strongly convex loss functions. The method attains the optimal $O(1/k^2)$ convergence rate, where $k$ is the iteration counter. The core of the work is the theoretical study of stepsize parameters. We have implemented the method on Archer - the largest supercomputer in the UK - and show that the method is capable of solving a (synthetic) LASSO optimization problem with 50 billion variables.

Fast Distributed Coordinate Descent for Non-Strongly Convex Losses

http://arxiv.org/pdf/1405.5300v2

Title Fast Distributed Coordinate Descent NonStrongly Convex Losses Summary propose efficient distributed randomized coordinate descent method minimizing regularized nonstrongly convex loss function method attains optimal O1k2 convergence rate k iteration counter core work theoretical study stepsize parameter implemented method Archer largest supercomputer UK show method capable solving synthetic LASSO optimization problem 50 billion variable Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Michele Dallachiesa', 'Charu Aggarwal', 'Themis Palpanas']

1405.5829v1

In many real applications that use and analyze networked data, the links in the network graph may be erroneous, or derived from probabilistic techniques. In such cases, the node classification problem can be challenging, since the unreliability of the links may affect the final results of the classification process. If the information about link reliability is not used explicitly, the classification accuracy in the underlying network may be affected adversely. In this paper, we focus on situations that require the analysis of the uncertainty that is present in the graph structure. We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen. We propose two techniques based on a Bayes model and automatic parameter selection, and show that the incorporation of uncertainty in the classification process as a first-class citizen is beneficial. We experimentally evaluate the proposed approach using different real data sets, and study the behavior of the algorithms under different conditions. The results demonstrate the effectiveness and efficiency of our approach.

Node Classification in Uncertain Graphs

http://arxiv.org/pdf/1405.5829v1

Title Node Classification Uncertain Graphs Summary many real application use analyze networked data link network graph may erroneous derived probabilistic technique case node classification problem challenging since unreliability link may affect final result classification process information link reliability used explicitly classification accuracy underlying network may affected adversely paper focus situation require analysis uncertainty present graph structure study novel problem node classification uncertain graph treating uncertainty firstclass citizen propose two technique based Bayes model automatic parameter selection show incorporation uncertainty classification process firstclass citizen beneficial experimentally evaluate proposed approach using different real data set study behavior algorithm different condition result demonstrate effectiveness efficiency approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fangfang Li', 'Guandong Xu', 'Longbing Cao']

1405.6223v1

The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations. To solve these challenges, the objective item attributes are incorporated as complementary information. However, most of the existing methods for inferring the relationships between items assume that the attributes are "independently and identically distributed (iid)", which does not always hold in reality. In fact, the attributes are more or less coupled with each other by some implicit relationships. Therefore, in this pa-per we propose an attribute-based coupled similarity measure to capture the implicit relationships between items. We then integrate the implicit item coupling into MF to form the Coupled Item-based Matrix Factorization (CIMF) model. Experimental results on two open data sets demonstrate that CIMF outperforms the benchmark methods.

Coupled Item-based Matrix Factorization

http://arxiv.org/pdf/1405.6223v1

Title Coupled Itembased Matrix Factorization Summary essence challenge cold start sparsity Recommender Systems RS extant technique Collaborative Filtering CF Matrix Factorization MF mainly rely useritem rating matrix sometimes informative enough predicting recommendation solve challenge objective item attribute incorporated complementary information However existing method inferring relationship item assume attribute independently identically distributed iid always hold reality fact attribute le coupled implicit relationship Therefore paper propose attributebased coupled similarity measure capture implicit relationship item integrate implicit item coupling MF form Coupled Itembased Matrix Factorization CIMF model Experimental result two open data set demonstrate CIMF outperforms benchmark method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dan Stowell', 'Mark D. Plumbley']

1405.6524v1

Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and "unsupervised", meaning it requires no manual data labelling, yet it can improve performance on "supervised" tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, with a random forest classifier. We demonstrate that MFCCs are of limited power in this context, leading to worse performance than the raw Mel spectral data. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain.

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

http://arxiv.org/pdf/1405.6524v1

Title Automatic largescale classification bird sound strongly improved unsupervised feature learning Summary Automatic specie classification bird sound computational tool increasing importance ecology conservation monitoring vocal communication study make classification useful practice crucial improve accuracy ensuring run big data scale Many approach use acoustic measure based spectrogramtype data Melfrequency cepstral coefficient MFCC feature represent manuallydesigned summary spectral information However recent work machine learning demonstrated feature learnt automatically data often outperform manuallydesigned feature transforms Feature learning performed large scale unsupervised meaning requires manual data labelling yet improve performance supervised task classification work introduce technique feature learning large volume bird sound recording inspired technique proven useful domain experimentally compare twelve different feature representation derived Mel spectrum six use technique using four large diverse database bird vocalisation random forest classifier demonstrate MFCCs limited power context leading worse performance raw Mel spectral data Conversely demonstrate unsupervised feature learning provides substantial boost MFCCs Mel spectrum without adding computational complexity model trained boost particularly notable singlelabel classification task large scale spectrotemporal activation learned procedure resemble spectrotemporal receptive field calculated avian primary auditory forebrain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Walid Krichene', 'Benjamin Drighès', 'Alexandre M. Bayen']

1408.0017v1

We study the repeated congestion game, in which multiple populations of players share resources, and make, at each iteration, a decentralized decision on which resources to utilize. We investigate the following question: given a model of how individual players update their strategies, does the resulting dynamics of strategy profiles converge to the set of Nash equilibria of the one-shot game? We consider in particular a model in which players update their strategies using algorithms with sublinear discounted regret. We show that the resulting sequence of strategy profiles converges to the set of Nash equilibria in the sense of Ces\`aro means. However, strong convergence is not guaranteed in general. We show that strong convergence can be guaranteed for a class of algorithms with a vanishing upper bound on discounted regret, and which satisfy an additional condition. We call such algorithms AREP algorithms, for Approximate REPlicator, as they can be interpreted as a discrete-time approximation of the replicator equation, which models the continuous-time evolution of population strategies, and which is known to converge for the class of congestion games. In particular, we show that the discounted Hedge algorithm belongs to the AREP class, which guarantees its strong convergence.

Learning Nash Equilibria in Congestion Games

http://arxiv.org/pdf/1408.0017v1

Title Learning Nash Equilibria Congestion Games Summary study repeated congestion game multiple population player share resource make iteration decentralized decision resource utilize investigate following question given model individual player update strategy resulting dynamic strategy profile converge set Nash equilibrium oneshot game consider particular model player update strategy using algorithm sublinear discounted regret show resulting sequence strategy profile converges set Nash equilibrium sense Cesaro mean However strong convergence guaranteed general show strong convergence guaranteed class algorithm vanishing upper bound discounted regret satisfy additional condition call algorithm AREP algorithm Approximate REPlicator interpreted discretetime approximation replicator equation model continuoustime evolution population strategy known converge class congestion game particular show discounted Hedge algorithm belongs AREP class guarantee strong convergence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zaid Albataineh', 'Fathi M. Salem']

1408.0193v1

We propose a frequency domain method based on robust independent component analysis (RICA) to address the multichannel Blind Source Separation (BSS) problem of convolutive speech mixtures in highly reverberant environments. We impose regularization processes to tackle the ill-conditioning problem of the covariance matrix and to mitigate the performance degradation in the frequency domain. We apply an algorithm to separate the source signals in adverse conditions, i.e. high reverberation conditions when short observation signals are available. Furthermore, we study the impact of several parameters on the performance of separation, e.g. overlapping ratio and window type of the frequency domain method. We also compare different techniques to solve the frequency-domain permutation ambiguity. Through simulations and real world experiments, we verify the superiority of the presented convolutive algorithm among other BSS algorithms, including recursive regularized ICA (RR ICA), independent vector analysis (IVA).

A RobustICA Based Algorithm for Blind Separation of Convolutive Mixtures

http://arxiv.org/pdf/1408.0193v1

Title RobustICA Based Algorithm Blind Separation Convolutive Mixtures Summary propose frequency domain method based robust independent component analysis RICA address multichannel Blind Source Separation BSS problem convolutive speech mixture highly reverberant environment impose regularization process tackle illconditioning problem covariance matrix mitigate performance degradation frequency domain apply algorithm separate source signal adverse condition ie high reverberation condition short observation signal available Furthermore study impact several parameter performance separation eg overlapping ratio window type frequency domain method also compare different technique solve frequencydomain permutation ambiguity simulation real world experiment verify superiority presented convolutive algorithm among BSS algorithm including recursive regularized ICA RR ICA independent vector analysis IVA Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yucheng Low', 'Joseph E. Gonzalez', 'Aapo Kyrola', 'Danny Bickson', 'Carlos E. Guestrin', 'Joseph Hellerstein']

1408.2041v1

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

GraphLab: A New Framework For Parallel Machine Learning

http://arxiv.org/pdf/1408.2041v1

Title GraphLab New Framework Parallel Machine Learning Summary Designing implementing efficient provably correct parallel machine learning ML algorithm challenging Existing highlevel parallel abstraction like MapReduce insufficiently expressive lowlevel tool like MPI Pthreads leave ML expert repeatedly solving design challenge targeting common pattern ML developed GraphLab improves upon abstraction like MapReduce compactly expressing asynchronous iterative algorithm sparse computational dependency ensuring data consistency achieving high degree parallel performance demonstrate expressiveness GraphLab framework designing implementing parallel version belief propagation Gibbs sampling CoEM Lasso Compressed Sensing show using GraphLab achieve excellent parallel performance large scale realworld problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Djallel Bouneffouf']

1408.2195v1

Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour.

R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems

http://arxiv.org/pdf/1408.2195v1

Title RUCB Contextual Bandit Algorithm RiskAware Recommender Systems Summary Mobile ContextAware Recommender Systems naturally modelled explorationexploitation tradeoff exrexp problem system choose maximizing expected reward dealing current knowledge exploitation learning unknown user preference improve knowledge exploration problem addressed reinforcement learning community consider risk level current user situation may dangerous recommend item user may desire current situation risk level high introduce paper algorithm named RUCB considers risk level user situation adaptively balance exr exp detailed analysis experimental result reveals several important discovery exrexp behaviour Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Colin Taylor', 'Kalyan Veeramachaneni', "Una-May O'Reilly"]

1408.3382v1

Understanding why students stopout will help in understanding how students learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we build accurate predictive models of MOOC student stopout. We document a scalable, stopout prediction methodology, end to end, from raw source data to model analysis. We attempted to predict stopout for the Fall 2012 offering of 6.002x. This involved the meticulous and crowd-sourced engineering of over 25 predictive features extracted for thousands of students, the creation of temporal and non-temporal data representations for use in predictive modeling, the derivation of over 10 thousand models with a variety of state-of-the-art machine learning techniques and the analysis of feature importance by examining over 70000 models. We found that stop out prediction is a tractable problem. Our models achieved an AUC (receiver operating characteristic area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one week in advance. Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks' data, the models attained AUCs of 0.7.

Likely to stop? Predicting Stopout in Massive Open Online Courses

http://arxiv.org/pdf/1408.3382v1

Title Likely stop Predicting Stopout Massive Open Online Courses Summary Understanding student stopout help understanding student learn MOOCs report part 3 unit compendium describe build accurate predictive model MOOC student stopout document scalable stopout prediction methodology end end raw source data model analysis attempted predict stopout Fall 2012 offering 6002x involved meticulous crowdsourced engineering 25 predictive feature extracted thousand student creation temporal nontemporal data representation use predictive modeling derivation 10 thousand model variety stateoftheart machine learning technique analysis feature importance examining 70000 model found stop prediction tractable problem model achieved AUC receiver operating characteristic areaunderthecurve high 095 generally 088 predicting one week advance Even difficult prediction problem predicting stop end course one week data model attained AUCs 07 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kun Li', 'Max Q. -H. Meng']

1408.3727v5

In a robot-centered smart home, the robot observes the home states with its own sensors, and then it can change certain object states according to an operator's commands for remote operations, or imitate the operator's behaviors in the house for autonomous operations. To model the robot's imitation of the operator's behaviors in a dynamic indoor environment, we use multi-relational chains to describe the changes of environment states, and apply inverse reinforcement learning to encoding the operator's behaviors with a learned reward function. We implement this approach with a mobile robot, and do five experiments to include increasing training days, object numbers, and action types. Besides, a baseline method by directly recording the operator's behaviors is also implemented, and comparison is made on the accuracy of home state evaluation and the accuracy of robot action selection. The results show that the proposed approach handles dynamic environment well, and guides the robot's actions in the house more accurately.

Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home

http://arxiv.org/pdf/1408.3727v5

Title Inverse Reinforcement Learning MultiRelational Chains RobotCentered Smart Home Summary robotcentered smart home robot observes home state sensor change certain object state according operator command remote operation imitate operator behavior house autonomous operation model robot imitation operator behavior dynamic indoor environment use multirelational chain describe change environment state apply inverse reinforcement learning encoding operator behavior learned reward function implement approach mobile robot five experiment include increasing training day object number action type Besides baseline method directly recording operator behavior also implemented comparison made accuracy home state evaluation accuracy robot action selection result show proposed approach handle dynamic environment well guide robot action house accurately Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pierre-François Marteau', 'Sylvie Gibet', 'Clement Reverdy']

1408.3944v2

In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.

Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures

http://arxiv.org/pdf/1408.3944v2

Title DownSampling coupled Elastic Kernel Machines Efficient Recognition Isolated Gestures Summary field gestural action recognition many study focused dimensionality reduction along spatial axis reduce variability gestural sequence expressed reduced space computational complexity processing noticeable method explicitly addressed dimensionality reduction along time axis however major issue regard use elastic distance characterized quadratic complexity partially fill apparent gap present paper approach based temporal downsampling associated elastic kernel machine learning experimentally show two data set widely referenced domain human gesture recognition different term quality motion capture possible significantly reduce number skeleton frame maintaining good recognition rate method prof give satisfactory result level currently reached stateoftheart method data set computational complexity reduction make approach eligible realtime application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhensong Qian', 'Oliver Schulte', 'Yan Sun']

1408.5389v1

Databases contain information about which relationships do and do not hold among entities. To make this information accessible for statistical analysis requires computing sufficient statistics that combine information from different database tables. Such statistics may involve any number of {\em positive and negative} relationships. With a naive enumeration approach, computing sufficient statistics for negative relationships is feasible only for small databases. We solve this problem with a new dynamic programming algorithm that performs a virtual join, where the requisite counts are computed without materializing join tables. Contingency table algebra is a new extension of relational algebra, that facilitates the efficient implementation of this M\"obius virtual join operation. The M\"obius Join scales to large datasets (over 1M tuples) with complex schemas. Empirical evaluation with seven benchmark datasets showed that information about the presence and absence of links can be exploited in feature selection, association rule mining, and Bayesian network learning.

Computing Multi-Relational Sufficient Statistics for Large Databases

http://arxiv.org/pdf/1408.5389v1

Title Computing MultiRelational Sufficient Statistics Large Databases Summary Databases contain information relationship hold among entity make information accessible statistical analysis requires computing sufficient statistic combine information different database table statistic may involve number em positive negative relationship naive enumeration approach computing sufficient statistic negative relationship feasible small database solve problem new dynamic programming algorithm performs virtual join requisite count computed without materializing join table Contingency table algebra new extension relational algebra facilitates efficient implementation Mobius virtual join operation Mobius Join scale large datasets 1M tuples complex schema Empirical evaluation seven benchmark datasets showed information presence absence link exploited feature selection association rule mining Bayesian network learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Reza Arablouei', 'Kutluyıl Doğançay', 'Stefan Werner']

1408.6141v2

We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the inverse power method and the dichotomous coordinate-descent (DCD) iterations. The proposed algorithm, called DCD-RTLS, outperforms the previously-proposed RTLS algorithms, which are based on the line-search method, with reduced computational complexity. We perform a comprehensive analysis of the DCD-RTLS algorithm and show that it is asymptotically unbiased as well as being stable in the mean. We also find a lower bound for the forgetting factor that ensures mean-square stability of the algorithm and calculate the theoretical steady-state mean-square deviation (MSD). We verify the effectiveness of the proposed algorithm and the accuracy of the predicted steady-state MSD via simulations.

Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations

http://arxiv.org/pdf/1408.6141v2

Title Recursive Total LeastSquares Algorithm Based Inverse Power Method Dichotomous CoordinateDescent Iterations Summary develop recursive total leastsquares RTLS algorithm errorsinvariables system identification utilizing inverse power method dichotomous coordinatedescent DCD iteration proposed algorithm called DCDRTLS outperforms previouslyproposed RTLS algorithm based linesearch method reduced computational complexity perform comprehensive analysis DCDRTLS algorithm show asymptotically unbiased well stable mean also find lower bound forgetting factor ensures meansquare stability algorithm calculate theoretical steadystate meansquare deviation MSD verify effectiveness proposed algorithm accuracy predicted steadystate MSD via simulation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xing He', 'Robert Caiming Qiu', 'Qian Ai', 'Yinshuang Cao', 'Jie Gu', 'Zhijian Jin']

1502.00060v2

Power systems are developing very fast nowadays, both in size and in complexity; this situation is a challenge for Early Event Detection (EED). This paper proposes a data- driven unsupervised learning method to handle this challenge. Specifically, the random matrix theories (RMTs) are introduced as the statistical foundations for random matrix models (RMMs); based on the RMMs, linear eigenvalue statistics (LESs) are defined via the test functions as the system indicators. By comparing the values of the LES between the experimental and the theoretical ones, the anomaly detection is conducted. Furthermore, we develop 3D power-map to visualize the LES; it provides a robust auxiliary decision-making mechanism to the operators. In this sense, the proposed method conducts EED with a pure statistical procedure, requiring no knowledge of system topologies, unit operation/control models, etc. The LES, as a key ingredient during this procedure, is a high dimensional indictor derived directly from raw data. As an unsupervised learning indicator, the LES is much more sensitive than the low dimensional indictors obtained from supervised learning. With the statistical procedure, the proposed method is universal and fast; moreover, it is robust against traditional EED challenges (such as error accumulations, spurious correlations, and even bad data in core area). Case studies, with both simulated data and real ones, validate the proposed method. To manage large-scale distributed systems, data fusion is mentioned as another data processing ingredient.

A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid

http://arxiv.org/pdf/1502.00060v2

Title Random Matrix Theoretical Approach Early Event Detection Smart Grid Summary Power system developing fast nowadays size complexity situation challenge Early Event Detection EED paper proposes data driven unsupervised learning method handle challenge Specifically random matrix theory RMTs introduced statistical foundation random matrix model RMMs based RMMs linear eigenvalue statistic LESs defined via test function system indicator comparing value LES experimental theoretical one anomaly detection conducted Furthermore develop 3D powermap visualize LES provides robust auxiliary decisionmaking mechanism operator sense proposed method conduct EED pure statistical procedure requiring knowledge system topology unit operationcontrol model etc LES key ingredient procedure high dimensional indictor derived directly raw data unsupervised learning indicator LES much sensitive low dimensional indictors obtained supervised learning statistical procedure proposed method universal fast moreover robust traditional EED challenge error accumulation spurious correlation even bad data core area Case study simulated data real one validate proposed method manage largescale distributed system data fusion mentioned another data processing ingredient Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Roman Dovgopol', 'Matt Nohelty']

1502.00094v1

The rise in popularity of microblogging services like Twitter has led to increased use of content annotation strategies like the hashtag. Hashtags provide users with a tagging mechanism to help organize, group, and create visibility for their posts. This is a simple idea but can be challenging for the user in practice which leads to infrequent usage. In this paper, we will investigate various methods of recommending hashtags as new posts are created to encourage more widespread adoption and usage. Hashtag recommendation comes with numerous challenges including processing huge volumes of streaming data and content which is small and noisy. We will investigate preprocessing methods to reduce noise in the data and determine an effective method of hashtag recommendation based on the popular classification algorithms.

Twitter Hash Tag Recommendation

http://arxiv.org/pdf/1502.00094v1

Title Twitter Hash Tag Recommendation Summary rise popularity microblogging service like Twitter led increased use content annotation strategy like hashtag Hashtags provide user tagging mechanism help organize group create visibility post simple idea challenging user practice lead infrequent usage paper investigate various method recommending hashtags new post created encourage widespread adoption usage Hashtag recommendation come numerous challenge including processing huge volume streaming data content small noisy investigate preprocessing method reduce noise data determine effective method hashtag recommendation based popular classification algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Li Zhou']

1502.01057v1

Personalization is important for search engines to improve user experience. Most of the existing work do pure feature engineering and extract a lot of session-style features and then train a ranking model. Here we proposed a novel way to model both long term and short term user behavior using Multi-armed bandit algorithm. Our algorithm can generalize session information across users well, and as an Explore-Exploit style algorithm, it can generalize to new urls and new users well. Experiments show that our algorithm can improve performance over the default ranking and outperforms several popular Multi-armed bandit algorithms.

Personalized Web Search

http://arxiv.org/pdf/1502.01057v1

Title Personalized Web Search Summary Personalization important search engine improve user experience existing work pure feature engineering extract lot sessionstyle feature train ranking model proposed novel way model long term short term user behavior using Multiarmed bandit algorithm algorithm generalize session information across user well ExploreExploit style algorithm generalize new url new user well Experiments show algorithm improve performance default ranking outperforms several popular Multiarmed bandit algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Francesco Orabona']

1502.01632v1

I show a simple expression of the Mill's ratio of the Student's t-Distribution. I use it to prove Conjecture 1 in P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Mach. Learn., 47(2-3):235--256, May 2002.

A Simple Expression for Mill's Ratio of the Student's $t$-Distribution

http://arxiv.org/pdf/1502.01632v1

Title Simple Expression Mills Ratio Students tDistribution Summary show simple expression Mills ratio Students tDistribution use prove Conjecture 1 P Auer N CesaBianchi P Fischer Finitetime analysis multiarmed bandit problem Mach Learn 4723235256 May 2002 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Othman Soufan', 'Samer Arafat']

1502.01733v1

The aim of this paper is to propose an application of mutual information-based ensemble methods to the analysis and classification of heart beats associated with different types of Arrhythmia. Models of multilayer perceptrons, support vector machines, and radial basis function neural networks were trained and tested using the MIT-BIH arrhythmia database. This research brings a focus to an ensemble method that, to our knowledge, is a novel application in the area of ECG Arrhythmia detection. The proposed classifier ensemble method showed improved performance, relative to either majority voting classifier integration or to individual classifier performance. The overall ensemble accuracy was 98.25%.

Arrhythmia Detection using Mutual Information-Based Integration Method

http://arxiv.org/pdf/1502.01733v1

Title Arrhythmia Detection using Mutual InformationBased Integration Method Summary aim paper propose application mutual informationbased ensemble method analysis classification heart beat associated different type Arrhythmia Models multilayer perceptrons support vector machine radial basis function neural network trained tested using MITBIH arrhythmia database research brings focus ensemble method knowledge novel application area ECG Arrhythmia detection proposed classifier ensemble method showed improved performance relative either majority voting classifier integration individual classifier performance overall ensemble accuracy 9825 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yann N. Dauphin', 'Harm de Vries', 'Yoshua Bengio']

1502.04390v2

Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes. We show that the popular Jacobi preconditioner has undesirable behavior in the presence of both positive and negative curvature, and present theoretical and empirical evidence that the so-called equilibration preconditioner is comparatively better suited to non-convex problems. We introduce a novel adaptive learning rate scheme, called ESGD, based on the equilibration preconditioner. Our experiments show that ESGD performs as well or better than RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent.

Equilibrated adaptive learning rates for non-convex optimization

http://arxiv.org/pdf/1502.04390v2

Title Equilibrated adaptive learning rate nonconvex optimization Summary Parameterspecific adaptive learning rate method computationally efficient way reduce illconditioning problem encountered training large deep network Following recent work strongly suggests critical point encountered training network saddle point find considering presence negative eigenvalue Hessian could help u design better suited adaptive learning rate scheme show popular Jacobi preconditioner undesirable behavior presence positive negative curvature present theoretical empirical evidence socalled equilibration preconditioner comparatively better suited nonconvex problem introduce novel adaptive learning rate scheme called ESGD based equilibration preconditioner experiment show ESGD performs well better RMSProp term convergence speed always clearly improving plain stochastic gradient descent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Reshef Meir', 'David Parkes']

1502.05056v1

We consider a recent innovative theory by Chastain et al. on the role of sex in evolution [PNAS'14]. In short, the theory suggests that the evolutionary process of gene recombination implements the celebrated multiplicative weights updates algorithm (MWUA). They prove that the population dynamics induced by sexual reproduction can be precisely modeled by genes that use MWUA as their learning strategy in a particular coordination game. The result holds in the environments of \emph{weak selection}, under the assumption that the population frequencies remain a product distribution. We revisit the theory, eliminating both the requirement of weak selection and any assumption on the distribution of the population. Removing the assumption of product distributions is crucial, since as we show, this assumption is inconsistent with the population dynamics. We show that the marginal allele distributions induced by the population dynamics precisely match the marginals induced by a multiplicative weights update algorithm in this general setting, thereby affirming and substantially generalizing these earlier results. We further revise the implications for convergence and utility or fitness guarantees in coordination games. In contrast to the claim of Chastain et al.[PNAS'14], we conclude that the sexual evolutionary dynamics does not entail any property of the population distribution, beyond those already implied by convergence.

On Sex, Evolution, and the Multiplicative Weights Update Algorithm

http://arxiv.org/pdf/1502.05056v1

Title Sex Evolution Multiplicative Weights Update Algorithm Summary consider recent innovative theory Chastain et al role sex evolution PNAS14 short theory suggests evolutionary process gene recombination implement celebrated multiplicative weight update algorithm MWUA prove population dynamic induced sexual reproduction precisely modeled gene use MWUA learning strategy particular coordination game result hold environment emphweak selection assumption population frequency remain product distribution revisit theory eliminating requirement weak selection assumption distribution population Removing assumption product distribution crucial since show assumption inconsistent population dynamic show marginal allele distribution induced population dynamic precisely match marginals induced multiplicative weight update algorithm general setting thereby affirming substantially generalizing earlier result revise implication convergence utility fitness guarantee coordination game contrast claim Chastain et alPNAS14 conclude sexual evolutionary dynamic entail property population distribution beyond already implied convergence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kashish Ara Shakil', 'Shadma Anis', 'Mansaf Alam']

1502.05167v1

Dengue is a life threatening disease prevalent in several developed as well as developing countries like India.In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Na\"ive Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC = 1, had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results

Dengue disease prediction using weka data mining tool

http://arxiv.org/pdf/1502.05167v1

Title Dengue disease prediction using weka data mining tool Summary Dengue life threatening disease prevalent several developed well developing country like IndiaIn paper discus various algorithm approach data mining utilized dengue disease prediction Data mining well known technique used health organization classification disease dengue diabetes cancer bioinformatics research proposed approach used WEKA 10 cross validation evaluate data compare result Weka extensive collection different machine learning data mining algorithm paper firstly classified dengue data set compared different data mining technique weka Explorer knowledge flow Experimenter interface Furthermore order validate approach used dengue dataset 108 instance weka used 99 row 18 attribute determine prediction disease accuracy using classification different algorithm find best performance main objective paper classify data assist user extracting useful information data easily identify suitable algorithm accurate predictive model finding paper concluded Naive Bayes J48 best performance algorithm classified accuracy achieved maximum accuracy 100 99 correctly classified instance maximum ROC 1 least mean absolute error took minimum time building model Explorer Knowledge flow result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andrea Esuli', 'Fabrizio Sebastiani']

1502.05491v2

We address the problem of \emph{quantification}, a supervised learning task whose goal is, given a class, to estimate the relative frequency (or \emph{prevalence}) of the class in a dataset of unlabelled items. Quantification has several applications in data and text mining, such as estimating the prevalence of positive reviews in a set of reviews of a given product, or estimating the prevalence of a given support issue in a dataset of transcripts of phone calls to tech support. So far, quantification has been addressed by learning a general-purpose classifier, counting the unlabelled items which have been assigned the class, and tuning the obtained counts according to some heuristics. In this paper we depart from the tradition of using general-purpose classifiers, and use instead a supervised learning model for \emph{structured prediction}, capable of generating classifiers directly optimized for the (multivariate and non-linear) function used for evaluating quantification accuracy. The experiments that we have run on 5500 binary high-dimensional datasets (averaging more than 14,000 documents each) show that this method is more accurate, more stable, and more efficient than existing, state-of-the-art quantification methods.

Optimizing Text Quantifiers for Multivariate Loss Functions

http://arxiv.org/pdf/1502.05491v2

Title Optimizing Text Quantifiers Multivariate Loss Functions Summary address problem emphquantification supervised learning task whose goal given class estimate relative frequency emphprevalence class dataset unlabelled item Quantification several application data text mining estimating prevalence positive review set review given product estimating prevalence given support issue dataset transcript phone call tech support far quantification addressed learning generalpurpose classifier counting unlabelled item assigned class tuning obtained count according heuristic paper depart tradition using generalpurpose classifier use instead supervised learning model emphstructured prediction capable generating classifier directly optimized multivariate nonlinear function used evaluating quantification accuracy experiment run 5500 binary highdimensional datasets averaging 14000 document show method accurate stable efficient existing stateoftheart quantification method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kalyan Nagaraj', 'Amulyashree Sridhar']

1502.05534v1

Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.

NeuroSVM: A Graphical User Interface for Identification of Liver Patients

http://arxiv.org/pdf/1502.05534v1

Title NeuroSVM Graphical User Interface Identification Liver Patients Summary Diagnosis liver infection preliminary stage important better treatment today scenario device like sensor used detection infection Accurate classification technique required automatic identification disease sample context study utilizes data mining approach classification liver patient healthy individual Four algorithm Naive Bayes Bagging Random forest SVM implemented classification using R platform improve accuracy classification hybrid NeuroSVM model developed using SVM feedforward artificial neural network ANN hybrid model tested performance using statistical parameter like root mean square error RMSE mean absolute percentage error MAPE model resulted prediction accuracy 9883 result suggested development hybrid model improved accuracy prediction serve medicinal community prediction liver disease among patient graphical user interface GUI developed using R GUI deployed package local repository R platform user perform prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Prashanth L. A.', 'Shalabh Bhatnagar', 'Michael Fu', 'Steve Marcus']

1502.05577v2

We present novel algorithms for simulation optimization using random directions stochastic approximation (RDSA). These include first-order (gradient) as well as second-order (Newton) schemes. We incorporate both continuous-valued as well as discrete-valued perturbations into both our algorithms. The former are chosen to be independent and identically distributed (i.i.d.) symmetric, uniformly distributed random variables (r.v.), while the latter are i.i.d., asymmetric, Bernoulli r.v.s. Our Newton algorithm, with a novel Hessian estimation scheme, requires N-dimensional perturbations and three loss measurements per iteration, whereas the simultaneous perturbation Newton search algorithm of [1] requires 2N-dimensional perturbations and four loss measurements per iteration. We prove the unbiasedness of both gradient and Hessian estimates and asymptotic (strong) convergence for both first-order and second-order schemes. We also provide asymptotic normality results, which in particular establish that the asymmetric Bernoulli variant of Newton RDSA method is better than 2SPSA of [1]. Numerical experiments are used to validate the theoretical results.

Adaptive system optimization using random directions stochastic approximation

http://arxiv.org/pdf/1502.05577v2

Title Adaptive system optimization using random direction stochastic approximation Summary present novel algorithm simulation optimization using random direction stochastic approximation RDSA include firstorder gradient well secondorder Newton scheme incorporate continuousvalued well discretevalued perturbation algorithm former chosen independent identically distributed iid symmetric uniformly distributed random variable rv latter iid asymmetric Bernoulli rv Newton algorithm novel Hessian estimation scheme requires Ndimensional perturbation three loss measurement per iteration whereas simultaneous perturbation Newton search algorithm 1 requires 2Ndimensional perturbation four loss measurement per iteration prove unbiasedness gradient Hessian estimate asymptotic strong convergence firstorder secondorder scheme also provide asymptotic normality result particular establish asymmetric Bernoulli variant Newton RDSA method better 2SPSA 1 Numerical experiment used validate theoretical result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Francesco Orabona', 'David Pal']

1502.05744v2

We design algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. We achieve adaptiveness to norms of loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. Our algorithms work for any decision set, bounded or unbounded. For unbounded decisions sets, these are the first truly adaptive algorithms for online linear optimization.

Scale-Free Algorithms for Online Linear Optimization

http://arxiv.org/pdf/1502.05744v2

Title ScaleFree Algorithms Online Linear Optimization Summary design algorithm online linear optimization optimal regret time need know upper lower bound norm loss vector achieve adaptiveness norm loss vector scale invariance ie algorithm make exactly decision sequence loss vector multiplied positive constant algorithm work decision set bounded unbounded unbounded decision set first truly adaptive algorithm online linear optimization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Atilim Gunes Baydin', 'Barak A. Pearlmutter', 'Alexey Andreyevich Radul', 'Jeffrey Mark Siskind']

1502.05767v4

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.

Automatic differentiation in machine learning: a survey

http://arxiv.org/pdf/1502.05767v4

Title Automatic differentiation machine learning survey Summary Derivatives mostly form gradient Hessians ubiquitous machine learning Automatic differentiation AD also called algorithmic differentiation simply autodiff family technique similar general backpropagation efficiently accurately evaluating derivative numeric function expressed computer program AD small established field application area including computational fluid dynamic atmospheric science engineering design optimization recently field machine learning AD largely unaware case independently discovered others result Despite relevance generalpurpose AD missing machine learning toolbox situation slowly changing ongoing adoption name dynamic computational graph differentiable programming survey intersection AD machine learning cover application AD direct relevance address main implementation technique precisely defining main differentiation technique interrelationship aim bring clarity usage term autodiff automatic differentiation symbolic differentiation encountered machine learning setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pierre Baqué', 'Jean-Hubert Hours', 'François Fleuret', 'Pascal Fua']

1502.05832v1

Mean-Field is an efficient way to approximate a posterior distribution in complex graphical models and constitutes the most popular class of Bayesian variational approximation methods. In most applications, the mean field distribution parameters are computed using an alternate coordinate minimization. However, the convergence properties of this algorithm remain unclear. In this paper, we show how, by adding an appropriate penalization term, we can guarantee convergence to a critical point, while keeping a closed form update at each step. A convergence rate estimate can also be derived based on recent results in non-convex optimization.

A provably convergent alternating minimization method for mean field inference

http://arxiv.org/pdf/1502.05832v1

Title provably convergent alternating minimization method mean field inference Summary MeanField efficient way approximate posterior distribution complex graphical model constitutes popular class Bayesian variational approximation method application mean field distribution parameter computed using alternate coordinate minimization However convergence property algorithm remain unclear paper show adding appropriate penalization term guarantee convergence critical point keeping closed form update step convergence rate estimate also derived based recent result nonconvex optimization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alexandros Ladas', 'Eamonn Ferguson', 'Uwe Aickelin', 'Jon Garibaldi']

1502.05911v1

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining.

A Data Mining framework to model Consumer Indebtedness with Psychological Factors

http://arxiv.org/pdf/1502.05911v1

Title Data Mining framework model Consumer Indebtedness Psychological Factors Summary Modelling Consumer Indebtedness proven problem complex nature work utilise Data Mining technique method explore multifaceted aspect Consumer Indebtedness examining contribution Psychological Factors like Impulsivity analysis Consumer Debt result confirm beneficial impact Psychological Factors modelling Consumer Indebtedness suggest new approach analysing Consumer Debt would take consideration Psychological characteristic consumer adopt technique practice Data Mining Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vladimir Vovk']

1502.06254v2

The standard loss functions used in the literature on probabilistic prediction are the log loss function, the Brier loss function, and the spherical loss function; however, any computable proper loss function can be used for comparison of prediction algorithms. This note shows that the log loss function is most selective in that any prediction algorithm that is optimal for a given data sequence (in the sense of the algorithmic theory of randomness) under the log loss function will be optimal under any computable proper mixable loss function; on the other hand, there is a data sequence and a prediction algorithm that is optimal for that sequence under either of the two other standard loss functions but not under the log loss function.

The fundamental nature of the log loss function

http://arxiv.org/pdf/1502.06254v2

Title fundamental nature log loss function Summary standard loss function used literature probabilistic prediction log loss function Brier loss function spherical loss function however computable proper loss function used comparison prediction algorithm note show log loss function selective prediction algorithm optimal given data sequence sense algorithmic theory randomness log loss function optimal computable proper mixable loss function hand data sequence prediction algorithm optimal sequence either two standard loss function log loss function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sébastien Bubeck', 'Ofer Dekel', 'Tomer Koren', 'Yuval Peres']

1502.06398v1

We analyze the minimax regret of the adversarial bandit convex optimization problem. Focusing on the one-dimensional case, we prove that the minimax regret is $\widetilde\Theta(\sqrt{T})$ and partially resolve a decade-old open problem. Our analysis is non-constructive, as we do not present a concrete algorithm that attains this regret rate. Instead, we use minimax duality to reduce the problem to a Bayesian setting, where the convex loss functions are drawn from a worst-case distribution, and then we solve the Bayesian version of the problem with a variant of Thompson Sampling. Our analysis features a novel use of convexity, formalized as a "local-to-global" property of convex functions, that may be of independent interest.

Bandit Convex Optimization: sqrt{T} Regret in One Dimension

http://arxiv.org/pdf/1502.06398v1

Title Bandit Convex Optimization sqrtT Regret One Dimension Summary analyze minimax regret adversarial bandit convex optimization problem Focusing onedimensional case prove minimax regret widetildeThetasqrtT partially resolve decadeold open problem analysis nonconstructive present concrete algorithm attains regret rate Instead use minimax duality reduce problem Bayesian setting convex loss function drawn worstcase distribution solve Bayesian version problem variant Thompson Sampling analysis feature novel use convexity formalized localtoglobal property convex function may independent interest Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Slimane Bellaouar', 'Hadda Cherroun', 'Djelloul Ziadi']

1502.07776v1

Kernel methods are powerful tools in machine learning. They have to be computationally efficient. In this paper, we present a novel Geometric-based approach to compute efficiently the string subsequence kernel (SSK). Our main idea is that the SSK computation reduces to range query problem. We started by the construction of a match list $L(s,t)=\{(i,j):s_{i}=t_{j}\}$ where $s$ and $t$ are the strings to be compared; such match list contains only the required data that contribute to the result. To compute efficiently the SSK, we extended the layered range tree data structure to a layered range sum tree, a range-aggregation data structure. The whole process takes $ O(p|L|\log|L|)$ time and $O(|L|\log|L|)$ space, where $|L|$ is the size of the match list and $p$ is the length of the SSK. We present empiric evaluations of our approach against the dynamic and the sparse programming approaches both on synthetically generated data and on newswire article data. Such experiments show the efficiency of our approach for large alphabet size except for very short strings. Moreover, compared to the sparse dynamic approach, the proposed approach outperforms absolutely for long strings.

Efficient Geometric-based Computation of the String Subsequence Kernel

http://arxiv.org/pdf/1502.07776v1

Title Efficient Geometricbased Computation String Subsequence Kernel Summary Kernel method powerful tool machine learning computationally efficient paper present novel Geometricbased approach compute efficiently string subsequence kernel SSK main idea SSK computation reduces range query problem started construction match list Lstijsitj string compared match list contains required data contribute result compute efficiently SSK extended layered range tree data structure layered range sum tree rangeaggregation data structure whole process take OpLlogL time OLlogL space L size match list p length SSK present empiric evaluation approach dynamic sparse programming approach synthetically generated data newswire article data experiment show efficiency approach large alphabet size except short string Moreover compared sparse dynamic approach proposed approach outperforms absolutely long string Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nahum Shimkin']

1503.00255v1

The notion of approachability in repeated games with vector payoffs was introduced by Blackwell in the 1950s, along with geometric conditions for approachability and corresponding strategies that rely on computing {\em steering directions} as projections from the current average payoff vector to the (convex) target set. Recently, Abernethy, Batlett and Hazan (2011) proposed a class of approachability algorithms that rely on the no-regret properties of Online Linear Programming for computing a suitable sequence of steering directions. This is first carried out for target sets that are convex cones, and then generalized to any convex set by embedding it in a higher-dimensional convex cone. In this paper we present a more direct formulation that relies on the support function of the set, along with suitable Online Convex Optimization algorithms, which leads to a general class of approachability algorithms. We further show that Blackwell's original algorithm and its convergence follow as a special case.

An Online Convex Optimization Approach to Blackwell's Approachability

http://arxiv.org/pdf/1503.00255v1

Title Online Convex Optimization Approach Blackwells Approachability Summary notion approachability repeated game vector payoff introduced Blackwell 1950s along geometric condition approachability corresponding strategy rely computing em steering direction projection current average payoff vector convex target set Recently Abernethy Batlett Hazan 2011 proposed class approachability algorithm rely noregret property Online Linear Programming computing suitable sequence steering direction first carried target set convex cone generalized convex set embedding higherdimensional convex cone paper present direct formulation relies support function set along suitable Online Convex Optimization algorithm lead general class approachability algorithm show Blackwells original algorithm convergence follow special case Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jenna Reps', 'Uwe Aickelin', 'Jonathan Garibaldi', 'Chris Damski']

1503.00587v1

Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule mining could be used to enable personalised targeting of mobile adverts with the aim of increasing the interaction rate. Over May and June 2014 we recorded advert interactions such as tapping the advert or watching the whole advert video along with the set of apps a user has installed at the time of the interaction. Based on the apps that the users have installed we applied k-means clustering to profile the users into one of ten classes. Due to the large number of apps considered we implemented dimension reduction to reduced the app feature space by mapping the apps to their iTunes category and clustered users based on the percentage of their apps that correspond to each iTunes app category. The clustering was externally validated by investigating differences between the way the ten profiles interact with the various adverts genres (lifestyle, finance and entertainment adverts). In addition association rule mining was performed to find whether the time of the day that the advert is served and the number of apps a user has installed makes certain profiles more likely to interact with the advert genres. The results showed there were clear differences in the way the profiles interact with the different advert genres and the results of this paper suggest that mobile advert targeting would improve the frequency that users interact with an advert.

Personalising Mobile Advertising Based on Users Installed Apps

http://arxiv.org/pdf/1503.00587v1

Title Personalising Mobile Advertising Based Users Installed Apps Summary Mobile advertising billion pound industry rapidly expanding success advert measured based user interact paper investigate whether application unsupervised learning association rule mining could used enable personalised targeting mobile advert aim increasing interaction rate May June 2014 recorded advert interaction tapping advert watching whole advert video along set apps user installed time interaction Based apps user installed applied kmeans clustering profile user one ten class Due large number apps considered implemented dimension reduction reduced app feature space mapping apps iTunes category clustered user based percentage apps correspond iTunes app category clustering externally validated investigating difference way ten profile interact various advert genre lifestyle finance entertainment advert addition association rule mining performed find whether time day advert served number apps user installed make certain profile likely interact advert genre result showed clear difference way profile interact different advert genre result paper suggest mobile advert targeting would improve frequency user interact advert Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Deepali Virmani', 'Shweta Taneja', 'Geetika Malhotra']

1503.00900v1

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing K-means clustering algorithm in terms of complexity and overall performance.

Normalization based K means Clustering Algorithm

http://arxiv.org/pdf/1503.00900v1

Title Normalization based K mean Clustering Algorithm Summary Kmeans effective clustering technique used separate similar data group based initial centroid cluster paper Normalization based Kmeans clustering algorithmNK mean proposed Proposed NK mean clustering algorithm applies normalization prior clustering available data well proposed approach calculates initial centroid based weight Experimental result prove betterment proposed NK mean clustering algorithm existing Kmeans clustering algorithm term complexity overall performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sanghyuk Chun', 'Yung-Kyun Noh', 'Jinwoo Shin']

1503.01578v2

Subspace clustering (SC) is a popular method for dimensionality reduction of high-dimensional data, where it generalizes Principal Component Analysis (PCA). Recently, several methods have been proposed to enhance the robustness of PCA and SC, while most of them are computationally very expensive, in particular, for high dimensional large-scale data. In this paper, we develop much faster iterative algorithms for SC, incorporating robustness using a {\em non-squared} $\ell_2$-norm objective. The known implementations for optimizing the objective would be costly due to the alternative optimization of two separate objectives: optimal cluster-membership assignment and robust subspace selection, while the substitution of one process to a faster surrogate can cause failure in convergence. To address the issue, we use a simplified procedure requiring efficient matrix-vector multiplications for subspace update instead of solving an expensive eigenvector problem at each iteration, in addition to release nested robust PCA loops. We prove that the proposed algorithm monotonically converges to a local minimum with approximation guarantees, e.g., it achieves 2-approximation for the robust PCA objective. In our experiments, the proposed algorithm is shown to converge at an order of magnitude faster than known algorithms optimizing the same objective, and have outperforms prior subspace clustering methods in accuracy and running time for MNIST dataset.

Scalable Iterative Algorithm for Robust Subspace Clustering

http://arxiv.org/pdf/1503.01578v2

Title Scalable Iterative Algorithm Robust Subspace Clustering Summary Subspace clustering SC popular method dimensionality reduction highdimensional data generalizes Principal Component Analysis PCA Recently several method proposed enhance robustness PCA SC computationally expensive particular high dimensional largescale data paper develop much faster iterative algorithm SC incorporating robustness using em nonsquared ell2norm objective known implementation optimizing objective would costly due alternative optimization two separate objective optimal clustermembership assignment robust subspace selection substitution one process faster surrogate cause failure convergence address issue use simplified procedure requiring efficient matrixvector multiplication subspace update instead solving expensive eigenvector problem iteration addition release nested robust PCA loop prove proposed algorithm monotonically converges local minimum approximation guarantee eg achieves 2approximation robust PCA objective experiment proposed algorithm shown converge order magnitude faster known algorithm optimizing objective outperforms prior subspace clustering method accuracy running time MNIST dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mike Wu']

1503.02328v1

Given financial data from popular sites like Yahoo and the London Exchange, the presented paper attempts to model and predict stocks that can be considered "good investments". Stocks are characterized by 125 features ranging from gross domestic product to EDIBTA, and are labeled by discrepancies between stock and market price returns. An artificial neural network (Self-Organizing Map) is fitted to train on more than a million data points to predict "good investments" given testing stocks from 2013 and after.

Financial Market Prediction

http://arxiv.org/pdf/1503.02328v1

Title Financial Market Prediction Summary Given financial data popular site like Yahoo London Exchange presented paper attempt model predict stock considered good investment Stocks characterized 125 feature ranging gross domestic product EDIBTA labeled discrepancy stock market price return artificial neural network SelfOrganizing Map fitted train million data point predict good investment given testing stock 2013 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mingkui Tan', 'Shijie Xiao', 'Junbin Gao', 'Dong Xu', 'Anton Van Den Hengel', 'Qinfeng Shi']

1503.02828v2

Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR). Solving this problem directly can be computationally expensive due to the unknown rank of variables or large-rank singular value decompositions (SVDs). To address this, we propose a proximal Riemannian gradient (PRG) scheme which can efficiently solve trace-norm regularized problems defined on real-algebraic variety $\mMLr$ of real matrices of rank at most $r$. Based on PRG, we further present a simple and novel subspace pursuit (SP) paradigm for general trace-norm regularized problems without the explicit rank constraint $\mMLr$. The proposed paradigm is very scalable by avoiding large-rank SVDs. Empirical studies on several tasks, such as matrix completion and LRR based subspace clustering, demonstrate the superiority of the proposed paradigms over existing methods.

Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient

http://arxiv.org/pdf/1503.02828v2

Title Scalable Nuclearnorm Minimization Subspace Pursuit Proximal Riemannian Gradient Summary Nuclearnorm regularization play vital role many learning task lowrank matrix recovery MR lowrank representation LRR Solving problem directly computationally expensive due unknown rank variable largerank singular value decomposition SVDs address propose proximal Riemannian gradient PRG scheme efficiently solve tracenorm regularized problem defined realalgebraic variety mMLr real matrix rank r Based PRG present simple novel subspace pursuit SP paradigm general tracenorm regularized problem without explicit rank constraint mMLr proposed paradigm scalable avoiding largerank SVDs Empirical study several task matrix completion LRR based subspace clustering demonstrate superiority proposed paradigm existing method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pranjal Awasthi', 'Maria-Florina Balcan', 'Nika Haghtalab', 'Ruth Urner']

1503.03594v1

We study the learnability of linear separators in $\Re^d$ in the presence of bounded (a.k.a Massart) noise. This is a realistic generalization of the random classification noise model, where the adversary can flip each example $x$ with probability $\eta(x) \leq \eta$. We provide the first polynomial time algorithm that can learn linear separators to arbitrarily small excess error in this noise model under the uniform distribution over the unit ball in $\Re^d$, for some constant value of $\eta$. While widely studied in the statistical learning theory community in the context of getting faster convergence rates, computationally efficient algorithms in this model had remained elusive. Our work provides the first evidence that one can indeed design algorithms achieving arbitrarily small excess error in polynomial time under this realistic noise model and thus opens up a new and exciting line of research. We additionally provide lower bounds showing that popular algorithms such as hinge loss minimization and averaging cannot lead to arbitrarily small excess error under Massart noise, even under the uniform distribution. Our work instead, makes use of a margin based technique developed in the context of active learning. As a result, our algorithm is also an active learning algorithm with label complexity that is only a logarithmic the desired excess error $\epsilon$.

Efficient Learning of Linear Separators under Bounded Noise

http://arxiv.org/pdf/1503.03594v1

Title Efficient Learning Linear Separators Bounded Noise Summary study learnability linear separator Red presence bounded aka Massart noise realistic generalization random classification noise model adversary flip example x probability etax leq eta provide first polynomial time algorithm learn linear separator arbitrarily small excess error noise model uniform distribution unit ball Red constant value eta widely studied statistical learning theory community context getting faster convergence rate computationally efficient algorithm model remained elusive work provides first evidence one indeed design algorithm achieving arbitrarily small excess error polynomial time realistic noise model thus open new exciting line research additionally provide lower bound showing popular algorithm hinge loss minimization averaging cannot lead arbitrarily small excess error Massart noise even uniform distribution work instead make use margin based technique developed context active learning result algorithm also active learning algorithm label complexity logarithmic desired excess error epsilon Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Elad Hazan', 'Kfir Y. Levy', 'Shai Shalev-Shwartz']

1503.03712v2

The graduated optimization approach, also known as the continuation method, is a popular heuristic to solving non-convex problems that has received renewed interest over the last decade. Despite its popularity, very little is known in terms of theoretical convergence analysis. In this paper we describe a new first-order algorithm based on graduated optimiza- tion and analyze its performance. We characterize a parameterized family of non- convex functions for which this algorithm provably converges to a global optimum. In particular, we prove that the algorithm converges to an {\epsilon}-approximate solution within O(1/\epsilon^2) gradient-based steps. We extend our algorithm and analysis to the setting of stochastic non-convex optimization with noisy gradient feedback, attaining the same convergence rate. Additionally, we discuss the setting of zero-order optimization, and devise a a variant of our algorithm which converges at rate of O(d^2/\epsilon^4).

On Graduated Optimization for Stochastic Non-Convex Problems

http://arxiv.org/pdf/1503.03712v2

Title Graduated Optimization Stochastic NonConvex Problems Summary graduated optimization approach also known continuation method popular heuristic solving nonconvex problem received renewed interest last decade Despite popularity little known term theoretical convergence analysis paper describe new firstorder algorithm based graduated optimiza tion analyze performance characterize parameterized family non convex function algorithm provably converges global optimum particular prove algorithm converges epsilonapproximate solution within O1epsilon2 gradientbased step extend algorithm analysis setting stochastic nonconvex optimization noisy gradient feedback attaining convergence rate Additionally discus setting zeroorder optimization devise variant algorithm converges rate Od2epsilon4 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Raef Bassily', 'Adam Smith', 'Thomas Steinke', 'Jonathan Ullman']

1503.04843v2

Adaptivity is an important feature of data analysis---typically the choice of questions asked about a dataset depends on previous interactions with the same dataset. However, generalization error is typically bounded in a non-adaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. (STOC '15) and Hardt and Ullman (FOCS '14) initiated the formal study of this problem, and gave the first upper and lower bounds on the achievable generalization error for adaptive data analysis. Specifically, suppose there is an unknown distribution $\mathcal{P}$ and a set of $n$ independent samples $x$ is drawn from $\mathcal{P}$. We seek an algorithm that, given $x$ as input, "accurately" answers a sequence of adaptively chosen "queries" about the unknown distribution $\mathcal{P}$. How many samples $n$ must we draw from the distribution, as a function of the type of queries, the number of queries, and the desired level of accuracy? In this work we make two new contributions towards resolving this question: *We give upper bounds on the number of samples $n$ that are needed to answer statistical queries that improve over the bounds of Dwork et al. *We prove the first upper bounds on the number of samples required to answer more general families of queries. These include arbitrary low-sensitivity queries and the important class of convex risk minimization queries. As in Dwork et al., our algorithms are based on a connection between differential privacy and generalization error, but we feel that our analysis is simpler and more modular, which may be useful for studying these questions in the future.

More General Queries and Less Generalization Error in Adaptive Data Analysis

http://arxiv.org/pdf/1503.04843v2

Title General Queries Less Generalization Error Adaptive Data Analysis Summary Adaptivity important feature data analysistypically choice question asked dataset depends previous interaction dataset However generalization error typically bounded nonadaptive model question specified dataset drawn Recent work Dwork et al STOC 15 Hardt Ullman FOCS 14 initiated formal study problem gave first upper lower bound achievable generalization error adaptive data analysis Specifically suppose unknown distribution mathcalP set n independent sample x drawn mathcalP seek algorithm given x input accurately answer sequence adaptively chosen query unknown distribution mathcalP many sample n must draw distribution function type query number query desired level accuracy work make two new contribution towards resolving question give upper bound number sample n needed answer statistical query improve bound Dwork et al prove first upper bound number sample required answer general family query include arbitrary lowsensitivity query important class convex risk minimization query Dwork et al algorithm based connection differential privacy generalization error feel analysis simpler modular may useful studying question future Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sindhu Padakandla', 'Prabuchandran K. J', 'Shalabh Bhatnagar']

1503.04964v1

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Energy Sharing for Multiple Sensor Nodes with Finite Buffers

http://arxiv.org/pdf/1503.04964v1

Title Energy Sharing Multiple Sensor Nodes Finite Buffers Summary consider problem finding optimal energy sharing policy maximize network performance system comprising multiple sensor node single energy harvesting EH source Sensor node periodically sense random field generate data stored corresponding data queue EH source harness energy ambient energy source generated energy stored energy buffer Sensor node receive energy data transmission EH source EH source efficiently share stored energy among node order minimize longrun average delay data transmission formulate problem energy sharing node framework average cost infinitehorizon Markov decision process MDPs develop efficient energy sharing algorithm namely Qlearning algorithm exploration mechanism based epsilongreedy method well upper confidence bound UCB extend algorithm incorporating state action space aggregation tackle stateaction space explosion MDP also develop cross entropy based method incorporates policy parameterization order find near optimal energy sharing policy simulation show algorithm yield energy sharing policy outperform heuristic greedy method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kai Li', 'Guojun Qi', 'Jun Ye', 'Kien A. Hua']

1503.05951v1

The era of Big Data has spawned unprecedented interests in developing hashing algorithms for efficient storage and fast nearest neighbor search. Most existing work learn hash functions that are numeric quantizations of feature values in projected feature space. In this work, we propose a novel hash learning framework that encodes feature's rank orders instead of numeric values in a number of optimal low-dimensional ranking subspaces. We formulate the ranking subspace learning problem as the optimization of a piece-wise linear convex-concave function and present two versions of our algorithm: one with independent optimization of each hash bit and the other exploiting a sequential learning framework. Our work is a generalization of the Winner-Take-All (WTA) hash family and naturally enjoys all the numeric stability benefits of rank correlation measures while being optimized to achieve high precision at very short code length. We compare with several state-of-the-art hashing algorithms in both supervised and unsupervised domain, showing superior performance in a number of data sets.

Rank Subspace Learning for Compact Hash Codes

http://arxiv.org/pdf/1503.05951v1

Title Rank Subspace Learning Compact Hash Codes Summary era Big Data spawned unprecedented interest developing hashing algorithm efficient storage fast nearest neighbor search existing work learn hash function numeric quantization feature value projected feature space work propose novel hash learning framework encodes feature rank order instead numeric value number optimal lowdimensional ranking subspace formulate ranking subspace learning problem optimization piecewise linear convexconcave function present two version algorithm one independent optimization hash bit exploiting sequential learning framework work generalization WinnerTakeAll WTA hash family naturally enjoys numeric stability benefit rank correlation measure optimized achieve high precision short code length compare several stateoftheart hashing algorithm supervised unsupervised domain showing superior performance number data set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matthias Boehm']

1503.06384v1

Declarative large-scale machine learning (ML) aims at the specification of ML algorithms in a high-level language and automatic generation of hybrid runtime execution plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks like Spark. The compilation of large-scale ML programs exhibits many opportunities for automatic optimization. Advanced cost-based optimization techniques require---as a fundamental precondition---an accurate cost model for evaluating the impact of optimization decisions. In this paper, we share insights into a simple and robust yet accurate technique for costing alternative runtime execution plans of ML programs. Our cost model relies on generating and costing runtime plans in order to automatically reflect all successive optimization phases. Costing runtime plans also captures control flow structures such as loops and branches, and a variety of cost factors like IO, latency, and computation costs. Finally, we linearize all these cost factors into a single measure of expected execution time. Within SystemML, this cost model is leveraged by several advanced optimizers like resource optimization and global data flow optimization. We share our lessons learned in order to provide foundations for the optimization of ML programs.

Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs

http://arxiv.org/pdf/1503.06384v1

Title Costing Generated Runtime Execution Plans LargeScale Machine Learning Programs Summary Declarative largescale machine learning ML aim specification ML algorithm highlevel language automatic generation hybrid runtime execution plan ranging single node inmemory computation distributed computation MapReduce MR similar framework like Spark compilation largescale ML program exhibit many opportunity automatic optimization Advanced costbased optimization technique requireas fundamental preconditionan accurate cost model evaluating impact optimization decision paper share insight simple robust yet accurate technique costing alternative runtime execution plan ML program cost model relies generating costing runtime plan order automatically reflect successive optimization phase Costing runtime plan also capture control flow structure loop branch variety cost factor like IO latency computation cost Finally linearize cost factor single measure expected execution time Within SystemML cost model leveraged several advanced optimizers like resource optimization global data flow optimization share lesson learned order provide foundation optimization ML program Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yossi Arjevani', 'Shai Shalev-Shwartz', 'Ohad Shamir']

1503.06833v1

We develop a novel framework to study smooth and strongly convex optimization algorithms, both deterministic and stochastic. Focusing on quadratic functions we are able to examine optimization algorithms as a recursive application of linear operators. This, in turn, reveals a powerful connection between a class of optimization algorithms and the analytic theory of polynomials whereby new lower and upper bounds are derived. Whereas existing lower bounds for this setting are only valid when the dimensionality scales with the number of iterations, our lower bound holds in the natural regime where the dimensionality is fixed. Lastly, expressing it as an optimal solution for the corresponding optimization problem over polynomials, as formulated by our framework, we present a novel systematic derivation of Nesterov's well-known Accelerated Gradient Descent method. This rather natural interpretation of AGD contrasts with earlier ones which lacked a simple, yet solid, motivation.

On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems

http://arxiv.org/pdf/1503.06833v1

Title Lower Upper Bounds Smooth Strongly Convex Optimization Problems Summary develop novel framework study smooth strongly convex optimization algorithm deterministic stochastic Focusing quadratic function able examine optimization algorithm recursive application linear operator turn reveals powerful connection class optimization algorithm analytic theory polynomial whereby new lower upper bound derived Whereas existing lower bound setting valid dimensionality scale number iteration lower bound hold natural regime dimensionality fixed Lastly expressing optimal solution corresponding optimization problem polynomial formulated framework present novel systematic derivation Nesterovs wellknown Accelerated Gradient Descent method rather natural interpretation AGD contrast earlier one lacked simple yet solid motivation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Freeha Azmat', 'Yunfei Chen', 'Nigel Stocks']

1503.07104v1

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.

Analysis of Spectrum Occupancy Using Machine Learning Algorithms

http://arxiv.org/pdf/1503.07104v1

Title Analysis Spectrum Occupancy Using Machine Learning Algorithms Summary paper analyze spectrum occupancy using different machine learning technique supervised technique naive Bayesian classifier NBC decision tree DT support vector machine SVM linear regression LR unsupervised algorithm hidden markov model HMM studied find best technique highest classification accuracy CA detailed comparison supervised unsupervised algorithm term computational time classification accuracy performed classified occupancy status utilized evaluate probability secondary user outage future time slot used system designer define spectrum allocation spectrum sharing policy Numerical result show SVM best algorithm among supervised unsupervised classifier Based proposed new SVM algorithm combining fire fly algorithm FFA shown outperform algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Azalia Mirhoseini', 'Eva L. Dyer', 'Ebrahim. M. Songhori', 'Richard G. Baraniuk', 'Farinaz Koushanfar']

1503.08169v2

This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets. Our framework exploits data structure to factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications. To demonstrate the utility of RankMap, we solve sparse recovery and power iteration problems on various real-world datasets with up to 1.8 billion non-zeros. Our evaluations are performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores. The results demonstrate up to two orders of magnitude improvements in memory usage, execution speed, and bandwidth compared with the best reported prior work, while achieving the same level of learning accuracy.

RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets

http://arxiv.org/pdf/1503.08169v2

Title RankMap PlatformAware Framework Distributed Learning Dense Datasets Summary paper introduces RankMap platformaware endtoend framework efficient execution broad class iterative learning algorithm massive dense datasets framework exploit data structure factorize ensemble lower rank subspace factorization creates sparse lowdimensional representation data property leveraged devise effective mapping scheduling iterative learning algorithm distributed computing machine provide two APIs one matrixbased one graphbased facilitate automated adoption framework performing several contemporary learning application demonstrate utility RankMap solve sparse recovery power iteration problem various realworld datasets 18 billion nonzeros evaluation performed Amazon EC2 IBM iDataPlex server using 244 core result demonstrate two order magnitude improvement memory usage execution speed bandwidth compared best reported prior work achieving level learning accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shiri Chechik', 'Edith Cohen', 'Haim Kaplan']

1503.08528v6

The average distance from a node to all other nodes in a graph, or from a query point in a metric space to a set of points, is a fundamental quantity in data analysis. The inverse of the average distance, known as the (classic) closeness centrality of a node, is a popular importance measure in the study of social networks. We develop novel structural insights on the sparsifiability of the distance relation via weighted sampling. Based on that, we present highly practical algorithms with strong statistical guarantees for fundamental problems. We show that the average distance (and hence the centrality) for all nodes in a graph can be estimated using $O(\epsilon^{-2})$ single-source distance computations. For a set $V$ of $n$ points in a metric space, we show that after preprocessing which uses $O(n)$ distance computations we can compute a weighted sample $S\subset V$ of size $O(\epsilon^{-2})$ such that the average distance from any query point $v$ to $V$ can be estimated from the distances from $v$ to $S$. Finally, we show that for a set of points $V$ in a metric space, we can estimate the average pairwise distance using $O(n+\epsilon^{-2})$ distance computations. The estimate is based on a weighted sample of $O(\epsilon^{-2})$ pairs of points, which is computed using $O(n)$ distance computations. Our estimates are unbiased with normalized mean square error (NRMSE) of at most $\epsilon$. Increasing the sample size by a $O(\log n)$ factor ensures that the probability that the relative error exceeds $\epsilon$ is polynomially small.

Average Distance Queries through Weighted Samples in Graphs and Metric Spaces: High Scalability with Tight Statistical Guarantees

http://arxiv.org/pdf/1503.08528v6

Title Average Distance Queries Weighted Samples Graphs Metric Spaces High Scalability Tight Statistical Guarantees Summary average distance node node graph query point metric space set point fundamental quantity data analysis inverse average distance known classic closeness centrality node popular importance measure study social network develop novel structural insight sparsifiability distance relation via weighted sampling Based present highly practical algorithm strong statistical guarantee fundamental problem show average distance hence centrality node graph estimated using Oepsilon2 singlesource distance computation set V n point metric space show preprocessing us distance computation compute weighted sample Ssubset V size Oepsilon2 average distance query point v V estimated distance v Finally show set point V metric space estimate average pairwise distance using Onepsilon2 distance computation estimate based weighted sample Oepsilon2 pair point computed using distance computation estimate unbiased normalized mean square error NRMSE epsilon Increasing sample size Olog n factor ensures probability relative error exceeds epsilon polynomially small Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Raphaël Liégeois', 'Bamdev Mishra', 'Mattia Zorzi', 'Rodolphe Sepulchre']

1503.08639v1

This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models. Based on the corresponding problem formulation recently presented, we use the alternating direction method of multipliers (ADMM) to efficiently solve it and scale it to sizes encountered in neuroimaging applications. We apply this decomposition on synthetic and real neuroimaging datasets with a specific focus on the information encoded in the low-rank structure of our model. In particular, we illustrate that this information captures the spatio-temporal structure of the original data, generalizing classical component analysis approaches.

Sparse plus low-rank autoregressive identification in neuroimaging time series

http://arxiv.org/pdf/1503.08639v1

Title Sparse plus lowrank autoregressive identification neuroimaging time series Summary paper considers problem identifying multivariate autoregressive AR sparse plus lowrank graphical model Based corresponding problem formulation recently presented use alternating direction method multiplier ADMM efficiently solve scale size encountered neuroimaging application apply decomposition synthetic real neuroimaging datasets specific focus information encoded lowrank structure model particular illustrate information capture spatiotemporal structure original data generalizing classical component analysis approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Juho Piironen', 'Aki Vehtari']

1503.08650v4

The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendations about the preferred approaches. We focus on the variable subset selection for regression and classification and perform several numerical experiments using both simulated and real world data. The results show that the optimization of a utility estimate such as the cross-validation (CV) score is liable to finding overfitted models due to relatively high variance in the utility estimates when the data is scarce. This can also lead to substantial selection induced bias and optimism in the performance evaluation for the selected model. From a predictive viewpoint, best results are obtained by accounting for model uncertainty by forming the full encompassing model, such as the Bayesian model averaging solution over the candidate models. If the encompassing model is too complex, it can be robustly simplified by the projection method, in which the information of the full model is projected onto the submodels. This approach is substantially less prone to overfitting than selection based on CV-score. Overall, the projection method appears to outperform also the maximum a posteriori model and the selection of the most probable variables. The study also demonstrates that the model selection can greatly benefit from using cross-validation outside the searching process both for guiding the model size selection and assessing the predictive performance of the finally selected model.

Comparison of Bayesian predictive methods for model selection

http://arxiv.org/pdf/1503.08650v4

Title Comparison Bayesian predictive method model selection Summary goal paper compare several widely used Bayesian model selection method practical model selection problem highlight difference give recommendation preferred approach focus variable subset selection regression classification perform several numerical experiment using simulated real world data result show optimization utility estimate crossvalidation CV score liable finding overfitted model due relatively high variance utility estimate data scarce also lead substantial selection induced bias optimism performance evaluation selected model predictive viewpoint best result obtained accounting model uncertainty forming full encompassing model Bayesian model averaging solution candidate model encompassing model complex robustly simplified projection method information full model projected onto submodels approach substantially le prone overfitting selection based CVscore Overall projection method appears outperform also maximum posteriori model selection probable variable study also demonstrates model selection greatly benefit using crossvalidation outside searching process guiding model size selection assessing predictive performance finally selected model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zimu Yuan', 'Zhiwei Xu']

1503.08818v1

Current digital currency schemes provide instantaneous exchange on precise commodity, in which "precise" means a buyer can possibly verify the function of the commodity without error. However, imprecise commodities, e.g. statistical data, with error existing are abundant in digital world. Existing digital currency schemes do not offer a mechanism to help the buyer for payment decision on precision of commodity, which may lead the buyer to a dilemma between having to buy and being unconfident. In this paper, we design a currency schemes IDCS for imprecise digital commodity. IDCS completes a trade in three stages of handshake between a buyer and providers. We present an IDCS prototype implementation that assigns weights on the trustworthy of the providers, and calculates a confidence level for the buyer to decide the quality of a imprecise commodity. In experiment, we characterize the performance of IDCS prototype under varying impact factors.

Founding Digital Currency on Imprecise Commodity

http://arxiv.org/pdf/1503.08818v1

Title Founding Digital Currency Imprecise Commodity Summary Current digital currency scheme provide instantaneous exchange precise commodity precise mean buyer possibly verify function commodity without error However imprecise commodity eg statistical data error existing abundant digital world Existing digital currency scheme offer mechanism help buyer payment decision precision commodity may lead buyer dilemma buy unconfident paper design currency scheme IDCS imprecise digital commodity IDCS completes trade three stage handshake buyer provider present IDCS prototype implementation assigns weight trustworthy provider calculates confidence level buyer decide quality imprecise commodity experiment characterize performance IDCS prototype varying impact factor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marta Arias', 'José L. Balcázar', 'Cristina Tîrnăucă']

1503.09025v3

A definite Horn theory is a set of n-dimensional Boolean vectors whose characteristic function is expressible as a definite Horn formula, that is, as conjunction of definite Horn clauses. The class of definite Horn theories is known to be learnable under different query learning settings, such as learning from membership and equivalence queries or learning from entailment. We propose yet a different type of query: the closure query. Closure queries are a natural extension of membership queries and also a variant, appropriate in the context of definite Horn formulas, of the so-called correction queries. We present an algorithm that learns conjunctions of definite Horn clauses in polynomial time, using closure and equivalence queries, and show how it relates to the canonical Guigues-Duquenne basis for implicational systems. We also show how the different query models mentioned relate to each other by either showing full-fledged reductions by means of query simulation (where possible), or by showing their connections in the context of particular algorithms that use them for learning definite Horn formulas.

Learning Definite Horn Formulas from Closure Queries

http://arxiv.org/pdf/1503.09025v3

Title Learning Definite Horn Formulas Closure Queries Summary definite Horn theory set ndimensional Boolean vector whose characteristic function expressible definite Horn formula conjunction definite Horn clause class definite Horn theory known learnable different query learning setting learning membership equivalence query learning entailment propose yet different type query closure query Closure query natural extension membership query also variant appropriate context definite Horn formula socalled correction query present algorithm learns conjunction definite Horn clause polynomial time using closure equivalence query show relates canonical GuiguesDuquenne basis implicational system also show different query model mentioned relate either showing fullfledged reduction mean query simulation possible showing connection context particular algorithm use learning definite Horn formula Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Seyed Mohammad Taheri', 'Hamidreza Mahyar', 'Mohammad Firouzi', 'Elahe Ghalebi K.', 'Radu Grosu', 'Ali Movaghar']

1612.01428v2

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction.

Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

http://arxiv.org/pdf/1612.01428v2

Title Extracting Implicit Social Relation Social Recommendation Techniques User Rating Prediction Summary Recommendation play increasingly important role daily life Recommender system automatically suggest item user might interesting Recent study illustrate incorporating social trust Matrix Factorization method demonstrably improves accuracy rating prediction approach mainly use trust score explicitly expressed user However often challenging user provide explicit trust score exist quite work propose Trust Metrics compute predict trust score user based interaction paper first present social relation extracted user rating item describing Hellinger distance user recommender system propose incorporate predicted trust score social matrix factorization model analyzing social relation extraction three wellknown realworld datasets trust recommendation data available conclude using implicit social relation social recommendation technique almost performance compared actual trust score explicitly expressed user Hence build method called HellTrustSVD top stateoftheart social recommendation technique incorporate extracted implicit social relation rating given user prediction item active user best knowledge first work extend TrustSVD extracted social trust information experimental result support idea employing implicit trust matrix factorization whenever explicit trust available perform much better stateoftheart approach user rating prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Celestine Dünner', 'Thomas Parnell', 'Kubilay Atasu', 'Manolis Sifalakis', 'Haralampos Pozidis']

1612.01437v2

In this paper we explore the performance limits of Apache Spark for machine learning applications. We begin by analyzing the characteristics of a state-of-the-art distributed machine learning algorithm implemented in Spark and compare it to an equivalent reference implementation using the high performance computing framework MPI. We identify critical bottlenecks of the Spark framework and carefully study their implications on the performance of the algorithm. In order to improve Spark performance we then propose a number of practical techniques to alleviate some of its overheads. However, optimizing computational efficiency and framework related overheads is not the only key to performance -- we demonstrate that in order to get the best performance out of any implementation it is necessary to carefully tune the algorithm to the respective trade-off between computation time and communication latency. The optimal trade-off depends on both the properties of the distributed algorithm as well as infrastructure and framework-related characteristics. Finally, we apply these technical and algorithmic optimizations to three different distributed linear machine learning algorithms that have been implemented in Spark. We present results using five large datasets and demonstrate that by using the proposed optimizations, we can achieve a reduction in the performance difference between Spark and MPI from 20x to 2x.

Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark

http://arxiv.org/pdf/1612.01437v2

Title Understanding Optimizing Performance Distributed Machine Learning Applications Apache Spark Summary paper explore performance limit Apache Spark machine learning application begin analyzing characteristic stateoftheart distributed machine learning algorithm implemented Spark compare equivalent reference implementation using high performance computing framework MPI identify critical bottleneck Spark framework carefully study implication performance algorithm order improve Spark performance propose number practical technique alleviate overhead However optimizing computational efficiency framework related overhead key performance demonstrate order get best performance implementation necessary carefully tune algorithm respective tradeoff computation time communication latency optimal tradeoff depends property distributed algorithm well infrastructure frameworkrelated characteristic Finally apply technical algorithmic optimization three different distributed linear machine learning algorithm implemented Spark present result using five large datasets demonstrate using proposed optimization achieve reduction performance difference Spark MPI 20x 2x Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['An Qu', 'Cheng Zhang', 'Paul Ackermann', 'Hedvig Kjellström']

1612.02490v1

Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy, such as after an Achilles Tendon Rupture (ATR). We formulate the problem of data imputation and prediction for ATR relevant medical measurements into a recommender system framework. By applying MatchBox, which is a collaborative filtering approach, on a real dataset collected from 374 ATR patients, we aim at offering personalized medical data imputation and prediction. In this work, we show the feasibility of this approach and discuss potential research directions by conducting initial qualitative evaluations.

Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation

http://arxiv.org/pdf/1612.02490v1

Title Bridging Medical Data Inference Achilles Tendon Rupture Rehabilitation Summary Imputing incomplete medical test predicting patient outcome crucial guiding decision making therapy Achilles Tendon Rupture ATR formulate problem data imputation prediction ATR relevant medical measurement recommender system framework applying MatchBox collaborative filtering approach real dataset collected 374 ATR patient aim offering personalized medical data imputation prediction work show feasibility approach discus potential research direction conducting initial qualitative evaluation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Homayun Afrabandpey', 'Tomi Peltola', 'Samuel Kaski']

1612.02802v2

Regression under the "small $n$, large $p$" conditions, of small sample size $n$ and large number of features $p$ in the learning data set, is a recurring setting in which learning from data is difficult. With prior knowledge about relationships of the features, $p$ can effectively be reduced, but explicating such prior knowledge is difficult for experts. In this paper we introduce a new method for eliciting expert prior knowledge about the similarity of the roles of features in the prediction task. The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters. Specifically, for learning to predict a target variable with Bayesian linear regression, the feature relationships are used to construct a Gaussian prior with a full covariance matrix for the regression coefficients. Evaluation of our method in experiments with simulated and real users on text data confirm that prior elicitation of feature similarities improves prediction accuracy. Furthermore, elicitation with an interactive scatterplot display outperforms straightforward elicitation where the users choose feature pairs from a feature list.

Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction

http://arxiv.org/pdf/1612.02802v2

Title Interactive Prior Elicitation Feature Similarities Small Sample Size Prediction Summary Regression small n large p condition small sample size n large number feature p learning data set recurring setting learning data difficult prior knowledge relationship feature p effectively reduced explicating prior knowledge difficult expert paper introduce new method eliciting expert prior knowledge similarity role feature prediction task key idea use interactive multidimensionalscaling MDS type scatterplot display feature elicit similarity relationship use elicited relationship prior distribution prediction parameter Specifically learning predict target variable Bayesian linear regression feature relationship used construct Gaussian prior full covariance matrix regression coefficient Evaluation method experiment simulated real user text data confirm prior elicitation feature similarity improves prediction accuracy Furthermore elicitation interactive scatterplot display outperforms straightforward elicitation user choose feature pair feature list Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Daniel Crankshaw', 'Xin Wang', 'Giulio Zhou', 'Michael J. Franklin', 'Joseph E. Gonzalez', 'Ion Stoica']

1612.03079v2

Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks and applications. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluate Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. Finally, we compare Clipper to the TensorFlow Serving system and demonstrate that we are able to achieve comparable throughput and latency while enabling model composition and online learning to improve accuracy and render more robust predictions.

Clipper: A Low-Latency Online Prediction Serving System

http://arxiv.org/pdf/1612.03079v2

Title Clipper LowLatency Online Prediction Serving System Summary Machine learning deployed growing number application demand realtime accurate robust prediction heavy query load However machine learning framework system address model training deployment paper introduce Clipper generalpurpose lowlatency prediction serving system Interposing enduser application wide range machine learning framework Clipper introduces modular architecture simplify model deployment across framework application Furthermore introducing caching batching adaptive model selection technique Clipper reduces prediction latency improves prediction throughput accuracy robustness without modifying underlying machine learning framework evaluate Clipper four common machine learning benchmark datasets demonstrate ability meet latency accuracy throughput demand online serving application Finally compare Clipper TensorFlow Serving system demonstrate able achieve comparable throughput latency enabling model composition online learning improve accuracy render robust prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zheng Xu', 'Soham De', 'Mario Figueiredo', 'Christoph Studer', 'Tom Goldstein']

1612.03349v1

The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems. We provide an empirical study of the practical performance of ADMM on several nonconvex applications, including l0 regularized linear regression, l0 regularized image denoising, phase retrieval, and eigenvector computation. Our experiments suggest that ADMM performs well on a broad class of non-convex problems. Moreover, recently proposed adaptive ADMM methods, which automatically tune penalty parameters as the method runs, can improve algorithm efficiency and solution quality compared to ADMM with a non-tuned penalty.

An Empirical Study of ADMM for Nonconvex Problems

http://arxiv.org/pdf/1612.03349v1

Title Empirical Study ADMM Nonconvex Problems Summary alternating direction method multiplier ADMM common optimization tool solving constrained nondifferentiable problem provide empirical study practical performance ADMM several nonconvex application including l0 regularized linear regression l0 regularized image denoising phase retrieval eigenvector computation experiment suggest ADMM performs well broad class nonconvex problem Moreover recently proposed adaptive ADMM method automatically tune penalty parameter method run improve algorithm efficiency solution quality compared ADMM nontuned penalty Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yiyan Wang', 'Haotian Xu', 'Zhijian Ou']

1612.04056v2

State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We are mainly motivated by the recent work of the joint Bayesian (JB) method, which is originally proposed for discriminant analysis in face verification. We apply JB to speaker verification and make three contributions beyond the original JB. 1) In contrast to the EM iterations with approximated statistics in the original JB, the EM iterations with exact statistics are employed and give better performance. 2) We propose to do simultaneous diagonalization (SD) of the within-class and between-class covariance matrices to achieve efficient testing, which has broader application scope than the SVD-based efficient testing method in the original JB. 3) We scrutinize similarities and differences between various Gaussian PLDAs and JB, complementing the previous analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are conducted on NIST SRE10 core condition 5, empirically validating the superiority of JB with faster convergence rate and 9-13% EER reduction compared with state-of-the-art PLDA.

Joint Bayesian Gaussian discriminant analysis for speaker verification

http://arxiv.org/pdf/1612.04056v2

Title Joint Bayesian Gaussian discriminant analysis speaker verification Summary Stateoftheart ivector based speaker verification relies variant Probabilistic Linear Discriminant Analysis PLDA discriminant analysis mainly motivated recent work joint Bayesian JB method originally proposed discriminant analysis face verification apply JB speaker verification make three contribution beyond original JB 1 contrast EM iteration approximated statistic original JB EM iteration exact statistic employed give better performance 2 propose simultaneous diagonalization SD withinclass betweenclass covariance matrix achieve efficient testing broader application scope SVDbased efficient testing method original JB 3 scrutinize similarity difference various Gaussian PLDAs JB complementing previous analysis comparing JB PrinceElder PLDA Extensive experiment conducted NIST SRE10 core condition 5 empirically validating superiority JB faster convergence rate 913 EER reduction compared stateoftheart PLDA Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sam Work']

1612.04108v1

This MSc dissertation considers the effects of the current corporate interest on researchers in the field of machine learning. Situated within the field's cyclical history of academic, public and corporate interest, this dissertation investigates how current researchers view recent developments and negotiate their own research practices within an environment of increased commercial interest and funding. The original research consists of in-depth interviews with 12 machine learning researchers working in both academia and industry. Building on theory from science, technology and society studies, this dissertation problematizes the traditional narratives of the neoliberalization of academic research by allowing the researchers themselves to discuss how their career choices, working environments and interactions with others in the field have been affected by the reinvigorated corporate interest of recent years.

Corporate Disruption in the Science of Machine Learning

http://arxiv.org/pdf/1612.04108v1

Title Corporate Disruption Science Machine Learning Summary MSc dissertation considers effect current corporate interest researcher field machine learning Situated within field cyclical history academic public corporate interest dissertation investigates current researcher view recent development negotiate research practice within environment increased commercial interest funding original research consists indepth interview 12 machine learning researcher working academia industry Building theory science technology society study dissertation problematizes traditional narrative neoliberalization academic research allowing researcher discus career choice working environment interaction others field affected reinvigorated corporate interest recent year Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuan Tang']

1612.04251v1

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

http://arxiv.org/pdf/1612.04251v1

Title TFLearn TensorFlows Highlevel Module Distributed Machine Learning Summary TFLearn highlevel Python module distributed machine learning inside TensorFlow provides easytouse Scikitlearn style interface simplify process creating configuring training evaluating experimenting machine learning model TFLearn integrates wide range stateofart machine learning algorithm built top TensorFlows low level APIs small largescale supervised unsupervised problem module focus bringing machine learning nonspecialists using generalpurpose highlevel language well researcher want implement benchmark compare new method structured environment Emphasis put ease use performance documentation API consistency Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ali Burak Ünal', 'Öznur Taştan']

1612.04431v2

Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations reveal that only a small fraction of the genes are altered frequently in patients. On the other hand different genomic alterations may perturb the same pathways. We propose a novel clustering procedure that quantifies the similarities of patients from their mutational profile on pathways via a novel graph kernel. We represent each KEGG pathway as an undirected graph. For each patient the vertex labels are assigned based on her altered genes. Smoothed shortest path graph kernel (smSPK) evaluates each pair of patients by comparing their vertex labeled pathway graphs. Our clustering procedure involves two steps: the smSPK kernel matrix derived for each pathway are input to kernel k-means algorithm and each pathway is evaluated individually. In the next step, only those pathways that are successful are combined in to a single kernel input to kernel k-means to stratify patients. Evaluating the procedure on simulated data showed that smSPK clusters patients up to 88\% accuracy. Finally to identify ovarian cancer patient subgroups, we apply our methodology to the cancer genome atlas ovarian data that involves 481 patients. The identified subgroups are evaluated through survival analysis. Grouping patients into four clusters results with patients groups that are significantly different in their survival times ($p$-value $\le 0.005$).

Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel

http://arxiv.org/pdf/1612.04431v2

Title Identification Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel Summary Characterizing patient somatic mutation nextgeneration sequencing technology open possibility refining cancer subtypes However catalogue mutation reveal small fraction gene altered frequently patient hand different genomic alteration may perturb pathway propose novel clustering procedure quantifies similarity patient mutational profile pathway via novel graph kernel represent KEGG pathway undirected graph patient vertex label assigned based altered gene Smoothed shortest path graph kernel smSPK evaluates pair patient comparing vertex labeled pathway graph clustering procedure involves two step smSPK kernel matrix derived pathway input kernel kmeans algorithm pathway evaluated individually next step pathway successful combined single kernel input kernel kmeans stratify patient Evaluating procedure simulated data showed smSPK cluster patient 88 accuracy Finally identify ovarian cancer patient subgroup apply methodology cancer genome atlas ovarian data involves 481 patient identified subgroup evaluated survival analysis Grouping patient four cluster result patient group significantly different survival time pvalue le 0005 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Filip Korzeniowski', 'Gerhard Widmer']

1612.05065v1

We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.

Feature Learning for Chord Recognition: The Deep Chroma Extractor

http://arxiv.org/pdf/1612.05065v1

Title Feature Learning Chord Recognition Deep Chroma Extractor Summary explore framelevel audio feature learning chord recognition using artificial neural network present argument chroma vector potentially hold enough information model harmonic content audio chord recognition standard chroma extractor compute noisy feature lead u propose learned chroma feature extractor based artificial neural network trained compute chroma feature encode harmonic information important chord recognition robust irrelevant interference achieve feeding network audio spectrum context instead single frame input way network learn selectively compensate noise resolve harmonic ambiguity compare resulting feature handcrafted one using simple linear framewise classifier chord recognition various data set result show learned feature extractor produce superior chroma vector chord recognition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Filip Korzeniowski', 'Gerhard Widmer']

1612.05082v1

Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.

A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

http://arxiv.org/pdf/1612.05082v1

Title Fully Convolutional Deep Auditory Model Musical Chord Recognition Summary Chord recognition system depend robust feature extraction pipeline pipeline traditionally handcrafted recent advance endtoend machine learning begun inspire researcher explore datadriven method task paper present chord recognition system us fully convolutional deep auditory model feature extraction extracted feature processed Conditional Random Field decodes final chord sequence processing stage trained automatically require expert knowledge optimising parameter show learned auditory system extract musically interpretable feature proposed chord recognition system achieves result par better stateoftheart algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rainer Kelz', 'Matthias Dorfer', 'Filip Korzeniowski', 'Sebastian Böck', 'Andreas Arzt', 'Gerhard Widmer']

1612.05153v1

In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription. We systematically compare different popular input representations for transcription systems to determine the ones most suitable for use with neural networks. Exploiting recent advances in training techniques and new regularizers, and taking into account hyper-parameter tuning, we show that it is possible, by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset -- without any complex post-processing steps. Thus, we propose this simple approach as a new baseline for this dataset, for future transcription research to build on and improve.

On the Potential of Simple Framewise Approaches to Piano Transcription

http://arxiv.org/pdf/1612.05153v1

Title Potential Simple Framewise Approaches Piano Transcription Summary attempt exploring limitation simple approach task piano transcription usually defined MIR conduct indepth analysis neural networkbased framewise transcription systematically compare different popular input representation transcription system determine one suitable use neural network Exploiting recent advance training technique new regularizers taking account hyperparameter tuning show possible simple bottomup framewise processing obtain piano transcriber outperforms current published state art publicly available MAPS dataset without complex postprocessing step Thus propose simple approach new baseline dataset future transcription research build improve Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pengfei Sun', 'Jun Qin']

1612.05369v2

In this paper, we propose an end-to-end neural network (NN) based EEG-speech (NES) modeling framework, in which three network structures are developed to map imagined EEG signals to phonemes. The proposed NES models incorporate a language model based EEG feature extraction layer, an acoustic feature mapping layer, and a restricted Boltzmann machine (RBM) based the feature learning layer. The NES models can jointly realize the representation of multichannel EEG signals and the projection of acoustic speech signals. Among three proposed NES models, two augmented networks utilize spoken EEG signals as either bias or gate information to strengthen the feature learning and translation of imagined EEG signals. Experimental results show that all three proposed NES models outperform the baseline support vector machine (SVM) method on EEG-speech classification. With respect to binary classification, our approach achieves comparable results relative to deep believe network approach.

Neural networks based EEG-Speech Models

http://arxiv.org/pdf/1612.05369v2

Title Neural network based EEGSpeech Models Summary paper propose endtoend neural network NN based EEGspeech NES modeling framework three network structure developed map imagined EEG signal phoneme proposed NES model incorporate language model based EEG feature extraction layer acoustic feature mapping layer restricted Boltzmann machine RBM based feature learning layer NES model jointly realize representation multichannel EEG signal projection acoustic speech signal Among three proposed NES model two augmented network utilize spoken EEG signal either bias gate information strengthen feature learning translation imagined EEG signal Experimental result show three proposed NES model outperform baseline support vector machine SVM method EEGspeech classification respect binary classification approach achieves comparable result relative deep believe network approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Clément Gaultier', 'Saurabh Kataria', 'Antoine Deleforge']

1612.06287v1

This paper introduces a new paradigm for sound source lo-calization referred to as virtual acoustic space traveling (VAST) and presents a first dataset designed for this purpose. Existing sound source localization methods are either based on an approximate physical model (physics-driven) or on a specific-purpose calibration set (data-driven). With VAST, the idea is to learn a mapping from audio features to desired audio properties using a massive dataset of simulated room impulse responses. This virtual dataset is designed to be maximally representative of the potential audio scenes that the considered system may be evolving in, while remaining reasonably compact. We show that virtually-learned mappings on this dataset generalize to real data, overcoming some intrinsic limitations of traditional binaural sound localization methods based on time differences of arrival.

VAST : The Virtual Acoustic Space Traveler Dataset

http://arxiv.org/pdf/1612.06287v1

Title VAST Virtual Acoustic Space Traveler Dataset Summary paper introduces new paradigm sound source localization referred virtual acoustic space traveling VAST present first dataset designed purpose Existing sound source localization method either based approximate physical model physicsdriven specificpurpose calibration set datadriven VAST idea learn mapping audio feature desired audio property using massive dataset simulated room impulse response virtual dataset designed maximally representative potential audio scene considered system may evolving remaining reasonably compact show virtuallylearned mapping dataset generalize real data overcoming intrinsic limitation traditional binaural sound localization method based time difference arrival Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xingzhong Du', 'Hongzhi Yin']

1612.06935v5

The online videos are generated at an unprecedented speed in recent years. As a result, how to generate personalized recommendation from the large volume of videos becomes more and more challenging. In this paper, we propose to extract the non-textual contents from the videos themselves to enhance the personalized video recommendation. The change of the content types makes us study three issues in this paper. The first issue is what non-textual contents are helpful. Considering the users are attracted by the videos in different aspects, multiple audio and visual features are extracted, encoded and transformed to represent the video contents in the recommender system for the first time. The second issue is how to use the non-textual contents to generate accurate personalized recommendation. We reproduce the existing methods and find that they do not perform well with the non-textual contents due to the mismatch between the features and the learning methods. To address this problem, we propose a new method in this paper. Our experiments show that the proposed method is more accurate whether the video content features are non-textual or textual.

Challenging Personalized Video Recommendation

http://arxiv.org/pdf/1612.06935v5

Title Challenging Personalized Video Recommendation Summary online video generated unprecedented speed recent year result generate personalized recommendation large volume video becomes challenging paper propose extract nontextual content video enhance personalized video recommendation change content type make u study three issue paper first issue nontextual content helpful Considering user attracted video different aspect multiple audio visual feature extracted encoded transformed represent video content recommender system first time second issue use nontextual content generate accurate personalized recommendation reproduce existing method find perform well nontextual content due mismatch feature learning method address problem propose new method paper experiment show proposed method accurate whether video content feature nontextual textual Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nam Khanh Tran']

1612.07117v1

In this paper, we propose two methods for tackling the problem of cross-device matching for online advertising at CIKM Cup 2016. The first method considers the matching problem as a binary classification task and solve it by utilizing ensemble learning techniques. The second method defines the matching problem as a ranking task and effectively solve it with using learning-to-rank algorithms. The results show that the proposed methods obtain promising results, in which the ranking-based method outperforms the classification-based method for the task.

Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016

http://arxiv.org/pdf/1612.07117v1

Title Classification Learningtorank Approaches CrossDevice Matching CIKM Cup 2016 Summary paper propose two method tackling problem crossdevice matching online advertising CIKM Cup 2016 first method considers matching problem binary classification task solve utilizing ensemble learning technique second method defines matching problem ranking task effectively solve using learningtorank algorithm result show proposed method obtain promising result rankingbased method outperforms classificationbased method task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Amir Daneshmand', 'Gesualdo Scutari', 'Francisco Facchinei']

1612.07335v1

The paper studies distributed Dictionary Learning (DL) problems where the learning task is distributed over a multi-agent network with time-varying (nonsymmetric) connectivity. This formulation is relevant, for instance, in big-data scenarios where massive amounts of data are collected/stored in different spatial locations and it is unfeasible to aggregate and/or process all the data in a fusion center, due to resource limitations, communication overhead or privacy considerations. We develop a general distributed algorithmic framework for the (nonconvex) DL problem and establish its asymptotic convergence. The new method hinges on Successive Convex Approximation (SCA) techniques coupled with i) a gradient tracking mechanism instrumental to locally estimate the missing global information; and ii) a consensus step, as a mechanism to distribute the computations among the agents. To the best of our knowledge, this is the first distributed algorithm with provable convergence for the DL problem and, more in general, bi-convex optimization problems over (time-varying) directed graphs.

Distributed Dictionary Learning

http://arxiv.org/pdf/1612.07335v1

Title Distributed Dictionary Learning Summary paper study distributed Dictionary Learning DL problem learning task distributed multiagent network timevarying nonsymmetric connectivity formulation relevant instance bigdata scenario massive amount data collectedstored different spatial location unfeasible aggregate andor process data fusion center due resource limitation communication overhead privacy consideration develop general distributed algorithmic framework nonconvex DL problem establish asymptotic convergence new method hinge Successive Convex Approximation SCA technique coupled gradient tracking mechanism instrumental locally estimate missing global information ii consensus step mechanism distribute computation among agent best knowledge first distributed algorithm provable convergence DL problem general biconvex optimization problem timevarying directed graph Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Johannes Blömer', 'Sascha Brauer', 'Kathrin Bujna']

1612.07516v2

The fuzzy $K$-means problem is a generalization of the $K$-means problem to soft clusterings. Although popular in practice, the first $(1+\epsilon)$-approximation algorithms for this problem have been proposed only recently. In this paper, we pursue the analysis of the fuzzy $K$-means problem further by making use of coresets. A coreset is an efficient representative of the input data set that preserves certain properties of the input, such as the quality of a solution. In this paper, we propose a novel proof technique for coresets, which makes use of the notion of weak coresets. Following this approach, we are able to show that small coresets for the fuzzy $K$-means problem exist. More precisely, we show that coresets with size poly-logarithmic in the number of input data points can be constructed in linear time. With these coresets, we are able to improve the asymptotic runtime of one of the best approximation algorithms for the fuzzy $K$-means problem.

On Coreset Constructions for the Fuzzy $K$-Means Problem

http://arxiv.org/pdf/1612.07516v2

Title Coreset Constructions Fuzzy KMeans Problem Summary fuzzy Kmeans problem generalization Kmeans problem soft clustering Although popular practice first 1epsilonapproximation algorithm problem proposed recently paper pursue analysis fuzzy Kmeans problem making use coresets coreset efficient representative input data set preserve certain property input quality solution paper propose novel proof technique coresets make use notion weak coresets Following approach able show small coresets fuzzy Kmeans problem exist precisely show coresets size polylogarithmic number input data point constructed linear time coresets able improve asymptotic runtime one best approximation algorithm fuzzy Kmeans problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marcell Vazquez-Chanlatte', 'Jyotirmoy V. Deshmukh', 'Xiaoqing Jin', 'Sanjit A. Seshia']

1612.07823v3

To effectively analyze and design cyberphysical systems (CPS), designers today have to combat the data deluge problem, i.e., the burden of processing intractably large amounts of data produced by complex models and experiments. In this work, we utilize monotonic Parametric Signal Temporal Logic (PSTL) to design features for unsupervised classification of time series data. This enables using off-the-shelf machine learning tools to automatically cluster similar traces with respect to a given PSTL formula. We demonstrate how this technique produces interpretable formulas that are amenable to analysis and understanding using a few representative examples. We illustrate this with case studies related to automotive engine testing, highway traffic analysis, and auto-grading massively open online courses.

Logic-based Clustering and Learning for Time-Series Data

http://arxiv.org/pdf/1612.07823v3

Title Logicbased Clustering Learning TimeSeries Data Summary effectively analyze design cyberphysical system CPS designer today combat data deluge problem ie burden processing intractably large amount data produced complex model experiment work utilize monotonic Parametric Signal Temporal Logic PSTL design feature unsupervised classification time series data enables using offtheshelf machine learning tool automatically cluster similar trace respect given PSTL formula demonstrate technique produce interpretable formula amenable analysis understanding using representative example illustrate case study related automotive engine testing highway traffic analysis autograding massively open online course Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Asim Kadav', 'Erik Kruus']

1612.08608v1

Emerging workloads, such as graph processing and machine learning are approximate because of the scale of data involved and the stochastic nature of the underlying algorithms. These algorithms are often distributed over multiple machines using bulk-synchronous processing (BSP) or other synchronous processing paradigms such as map-reduce. However, data parallel processing primitives such as repeated barrier and reduce operations introduce high synchronization overheads. Hence, many existing data-processing platforms use asynchrony and staleness to improve data-parallel job performance. Often, these systems simply change the synchronous communication to asynchronous between the worker nodes in the cluster. This improves the throughput of data processing but results in poor accuracy of the final output since different workers may progress at different speeds and process inconsistent intermediate outputs. In this paper, we present ASAP, a model that provides asynchronous and approximate processing semantics for data-parallel computation. ASAP provides fine-grained worker synchronization using NOTIFY-ACK semantics that allows independent workers to run asynchronously. ASAP also provides stochastic reduce that provides approximate but guaranteed convergence to the same result as an aggregated all-reduce. In our results, we show that ASAP can reduce synchronization costs and provides 2-10X speedups in convergence and up to 10X savings in network costs for distributed machine learning applications and provides strong convergence guarantees.

ASAP: Asynchronous Approximate Data-Parallel Computation

http://arxiv.org/pdf/1612.08608v1

Title ASAP Asynchronous Approximate DataParallel Computation Summary Emerging workload graph processing machine learning approximate scale data involved stochastic nature underlying algorithm algorithm often distributed multiple machine using bulksynchronous processing BSP synchronous processing paradigm mapreduce However data parallel processing primitive repeated barrier reduce operation introduce high synchronization overhead Hence many existing dataprocessing platform use asynchrony staleness improve dataparallel job performance Often system simply change synchronous communication asynchronous worker node cluster improves throughput data processing result poor accuracy final output since different worker may progress different speed process inconsistent intermediate output paper present ASAP model provides asynchronous approximate processing semantics dataparallel computation ASAP provides finegrained worker synchronization using NOTIFYACK semantics allows independent worker run asynchronously ASAP also provides stochastic reduce provides approximate guaranteed convergence result aggregated allreduce result show ASAP reduce synchronization cost provides 210X speedup convergence 10X saving network cost distributed machine learning application provides strong convergence guarantee Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chaoyun Zhang', 'Mingjun Zhong', 'Zongzuo Wang', 'Nigel Goddard', 'Charles Sutton']

1612.09106v3

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.

Sequence-to-point learning with neural networks for nonintrusive load monitoring

http://arxiv.org/pdf/1612.09106v3

Title Sequencetopoint learning neural network nonintrusive load monitoring Summary Energy disaggregation aka nonintrusive load monitoring NILM singlechannel blind source separation problem aim decompose main record whole house electricity consumption appliancewise reading problem difficult inherently unidentifiable Recent approach shown identifiability problem could reduced introducing domain knowledge model Deep neural network shown promising approach problem sliding window necessary handle long sequence arise signal processing problem raise issue combine prediction different sliding window paper propose sequencetopoint learning input window main output single point target appliance use convolutional neural network train model Interestingly systematically show convolutional neural network inherently learn signature target appliance automatically added model reduce identifiability problem applied proposed neural network approach realworld household energy data show method achieve stateoftheart performance improving two standard error measure 84 92 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andras Gyorgy', 'Tamas Linder', 'Gabor Lugosi', 'Gyorgy Ottucsak']

0704.1020v1

The on-line shortest path problem is considered under various models of partial monitoring. Given a weighted directed acyclic graph whose edge weights can change in an arbitrary (adversarial) way, a decision maker has to choose in each round of a game a path between two distinguished vertices such that the loss of the chosen path (defined as the sum of the weights of its composing edges) be as small as possible. In a setting generalizing the multi-armed bandit problem, after choosing a path, the decision maker learns only the weights of those edges that belong to the chosen path. For this problem, an algorithm is given whose average cumulative loss in n rounds exceeds that of the best path, matched off-line to the entire sequence of the edge weights, by a quantity that is proportional to 1/\sqrt{n} and depends only polynomially on the number of edges of the graph. The algorithm can be implemented with linear complexity in the number of rounds n and in the number of edges. An extension to the so-called label efficient setting is also given, in which the decision maker is informed about the weights of the edges corresponding to the chosen path at a total of m << n time instances. Another extension is shown where the decision maker competes against a time-varying path, a generalization of the problem of tracking the best expert. A version of the multi-armed bandit setting for shortest path is also discussed where the decision maker learns only the total weight of the chosen path but not the weights of the individual edges on the path. Applications to routing in packet switched networks along with simulation results are also presented.

The on-line shortest path problem under partial monitoring

http://arxiv.org/pdf/0704.1020v1

Title online shortest path problem partial monitoring Summary online shortest path problem considered various model partial monitoring Given weighted directed acyclic graph whose edge weight change arbitrary adversarial way decision maker choose round game path two distinguished vertex loss chosen path defined sum weight composing edge small possible setting generalizing multiarmed bandit problem choosing path decision maker learns weight edge belong chosen path problem algorithm given whose average cumulative loss n round exceeds best path matched offline entire sequence edge weight quantity proportional 1sqrtn depends polynomially number edge graph algorithm implemented linear complexity number round n number edge extension socalled label efficient setting also given decision maker informed weight edge corresponding chosen path total n time instance Another extension shown decision maker competes timevarying path generalization problem tracking best expert version multiarmed bandit setting shortest path also discussed decision maker learns total weight chosen path weight individual edge path Applications routing packet switched network along simulation result also presented Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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