OpenRaiser/Intern-Atlas huggingface dataset:
From labor to trader: opinion elicitation via online crowds as a market
Authors: Caleb Chen Cao, Lei Chen, H. V. Jagadish
Venue: KDD
Year: 2014

We often care about people's degrees of belief about certain events: e.g. causality between an action and the outcomes, odds distribution among the outcome of a horse race and so on. It is well recognized that the best form to elicit opinion from human is probability distribution instead of simple voting, because the form of distribution retains the delicate information that an opinion expresses. In the past, opinion elicitation has relied on experts, who are expensive and not always available. More recently, crowdsourcing has gained prominence as an inexpensive way to get a great deal of human input. However, traditional crowdsourcing has primarily focused on issuing very simple (e.g. binary decision) tasks to the crowd. In this paper, we study how to use crowds for Opinion Elicitation. There are three major challenges to eliciting opinion information in the form of probability distributions: how to measure the quality of distribution; how to aggregate the distributions; and, how to strategically implement such a system.
Leveraging user libraries to bootstrap collaborative filtering
Authors: Laurent Charlin, Richard S. Zemel, Hugo Larochelle
Venue: KDD
Year: 2014

We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM's chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. Overall, CSTM is a joint directed probabilistic model of user-item scores (ratings), and the textual side information in the user libraries and the items. Creating a generative description of scores and the text allows CSTM to perform well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many. Experiments on real-world datasets demonstrate CSTM's performance. We further demonstrate its utility in an application for personal recommendations of posters which we deployed at the NIPS 2013 conference.
Mining topics in documents: standing on the shoulders of big data
Authors: Zhiyuan Chen, Bing Liu
Venue: KDD
Year: 2014

Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning/modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines.
LWI-SVD: low-rank, windowed, incremental singular value decompositions on time-evolving data sets
Authors: Xilun Chen, K. Selçuk Candan
Venue: KDD
Year: 2014

Singular Value Decomposition (SVD) is computationally costly and therefore a naive implementation does not scale to the needs of scenarios where data evolves continuously. While there are various on-line analysis and incremental decomposition techniques, these may not accurately represent the data or may be slow for the needs of many applications. To address these challenges, in this paper, we propose a Low-rank, Windowed, Incremental SVD (LWI-SVD) algorithm, which (a) leverages efficient and accurate low-rank approximations to speed up incremental SVD updates and (b) uses a window-based approach to aggregate multiple incoming updates (insertions or deletions of rows and columns) and, thus, reduces on- line processing costs. We also present an LWI-SVD with restarts (LWI2-SVD) algorithm which leverages a novel highly efficient partial reconstruction based change detection scheme to support timely refreshing of the decomposition with significant changes in the data and prevent accumulation of errors over time. Experiment results, including comparisons to other state of the art techniques on different data sets and under different parameter settings, confirm that LWI-SVD and LWI2-SVD are both efficient and accurate in maintaining decompositions.
Fast flux discriminant for large-scale sparse nonlinear classification
Authors: Wenlin Chen, Yixin Chen, Kilian Q. Weinberger
Venue: KDD
Year: 2014

In this paper, we propose a novel supervised learning method, Fast Flux Discriminant (FFD), for large-scale nonlinear classification. Compared with other existing methods, FFD has unmatched advantages, as it attains the efficiency and interpretability of linear models as well as the accuracy of nonlinear models. It is also sparse and naturally handles mixed data types. It works by decomposing the kernel density estimation in the entire feature space into selected low-dimensional subspaces. Since there are many possible subspaces, we propose a submodular optimization framework for subspace selection. The selected subspace predictions are then transformed to new features on which a linear model can be learned. Besides, since the transformed features naturally expect non-negative weights, we only require smooth optimization even with the L1 regularization. Unlike other nonlinear models such as kernel methods, the FFD model is interpretable as it gives importance weights on the original features. Its training and testing are also much faster than traditional kernel models. We carry out extensive empirical studies on real-world datasets and show that the proposed model achieves state-of-the-art classification results with sparsity, interpretability, and exceptional scalability. Our model can be learned in minutes on datasets with millions of samples, for which most existing nonlinear methods will be prohibitively expensive in space and time.
Parallel gibbs sampling for hierarchical dirichlet processes via gamma processes equivalence
Authors: Dehua Cheng, Yan Liu
Venue: KDD
Year: 2014

The hierarchical Dirichlet process (HDP) is an intuitive and elegant technique to model data with latent groups. However, it has not been widely used for practical applications due to the high computational costs associated with inference. In this paper, we propose an effective parallel Gibbs sampling algorithm for HDP by exploring its connections with the gamma-gamma-Poisson process. Specifically, we develop a novel framework that combines bootstrap and Reversible Jump MCMC algorithm to enable parallel variable updates. We also provide theoretical convergence analysis based on Gibbs sampling with asynchronous variable updates. Experiment results on both synthetic datasets and two large-scale text collections show that our algorithm can achieve considerable speedup as well as better inference accuracy for HDP compared with existing parallel sampling algorithms.
Distance queries from sampled data: accurate and efficient
Authors: Edith Cohen
Venue: KDD
Year: 2014

Distance queries are a basic tool in data analysis. They are used for detection and localization of change for the purpose of anomaly detection, monitoring, or planning. Distance queries are particularly useful when data sets such as measurements, snapshots of a system, content, traffic matrices, and activity logs are collected repeatedly. Random sampling, which can be efficiently performed over streamed or distributed data, is an important tool for scalable data analysis. The sample constitutes an extremely flexible summary, which naturally supports domain queries and scalable estimation of statistics, which can be specified after the sample is generated. The effectiveness of a sample as a summary, however, hinges on the estimators we have. We derive novel estimators for estimating $L_p$ distance from sampled data. Our estimators apply with the most common weighted sampling schemes: Poisson Probability Proportional to Size (PPS) and its fixed sample size variants. They also apply when the samples of different data sets are independent or coordinated. Our estimators are admissible (Pareto optimal in terms of variance) and have compelling properties. We study the performance of our Manhattan and Euclidean distance (p=1,2) estimators on diverse datasets, demonstrating scalability and accuracy even when a small fraction of the data is sampled. Our work, for the first time, facilitates effective distance estimation over sampled data.
Data science through the lens of social science
Authors: Drew Conway
Venue: KDD
Year: 2014

In this talk, Drew will examine data science through the lens of the social scientist. He will discuss how the various skills and disciplines combine into data science. Drew will also present a motivating example directly from his work as a senior advisor to NYC's Mayor's Office of Analytics.
Empirical glitch explanations
Authors: Tamraparni Dasu, Ji Meng Loh, Divesh Srivastava
Venue: KDD
Year: 2014

Data glitches are unusual observations that do not conform to data quality expectations, be they logical, semantic or statistical. By applying data integrity constraints, potentially large sections of data could be flagged as being noncompliant. Ignoring or repairing significant sections of the data could fundamentally bias the results and conclusions drawn from analyses. In the context of Big Data where large numbers and volumes of feeds from disparate sources are integrated, it is likely that significant portions of seemingly noncompliant data are actually legitimate usable data.
Knock it off: profiling the online storefronts of counterfeit merchandise
Authors: Matthew F. Der, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
Venue: KDD
Year: 2014

We describe an automated system for the large-scale monitoring of Web sites that serve as online storefronts for spam-advertised goods. Our system is developed from an extensive crawl of black-market Web sites that deal in illegal pharmaceuticals, replica luxury goods, and counterfeit software. The operational goal of the system is to identify the affiliate programs of online merchants behind these Web sites; the system itself is part of a larger effort to improve the tracking and targeting of these affiliate programs. There are two main challenges in this domain. The first is that appearances can be deceiving: Web pages that render very differently are often linked to the same affiliate program of merchants. The second is the difficulty of acquiring training data: the manual labeling of Web pages, though necessary to some degree, is a laborious and time-consuming process. Our approach in this paper is to extract features that reveal when Web pages linked to the same affiliate program share a similar underlying structure. Using these features, which are mined from a small initial seed of labeled data, we are able to profile the Web sites of forty-four distinct affiliate programs that account, collectively, for hundreds of millions of dollars in illicit e-commerce. Our work also highlights several broad challenges that arise in the large-scale, empirical study of malicious activity on the Web.
Batch discovery of recurring rare classes toward identifying anomalous samples
Authors: Murat Dundar, Halid Ziya Yerebakan, Bartek Rajwa
Venue: KDD
Year: 2014

We present a clustering algorithm for discovering rare yet significant recurring classes across a batch of samples in the presence of random effects. We model each sample data by an infinite mixture of Dirichlet-process Gaussian-mixture models (DPMs) with each DPM representing the noisy realization of its corresponding class distribution in a given sample. We introduce dependencies across multiple samples by placing a global Dirichlet process prior over individual DPMs. This hierarchical prior introduces a sharing mechanism across samples and allows for identifying local realizations of classes across samples. We use collapsed Gibbs sampler for inference to recover local DPMs and identify their class associations. We demonstrate the utility of the proposed algorithm, processing a flow cytometry data set containing two extremely rare cell populations, and report results that significantly outperform competing techniques. The source code of the proposed algorithm is available on the web via the link: http://cs.iupui.edu/~dundar/aspire.htm.
Big data for social good
Authors: Nathan Eagle
Venue: KDD
Year: 2014

Petabytes of data about human movements, transactions and communication patterns are being generated by everyday technologies such as mobile phones & credit cards. This unprecedented volume of information facilitates a novel set of research questions applicable to a wide range of development issues. In collaboration involving 237 mobile phone operators across 102 countries, Jana's mobile technology platform can instantly poll and compensate 3.48 billion active mobile subscriptions. This talk will discuss how insights gained from living in Kenya became the genesis of a technology company currently working with global clients in over 50 countries, including P&G, Google, Unilever, Danone, General Mills, Nestle, Johnson & Johnson, Microsoft, the World Bank, and the United Nations. After providing an overview of the mobile and social media landscapes in emerging markets, we discuss a system that implements polls & mobile subscription compensation. The presentation will conclude by emphasizing the value of consumer data in underserved and understudied regions of the world.
A bayesian framework for estimating properties of network diffusions
Authors: Varun R. Embar, Rama Kumar Pasumarthi, Indrajit Bhattacharya
Venue: KDD
Year: 2014

The analysis of network connections, diffusion processes and cascades requires evaluating properties of the diffusion network. Properties of interest often involve variables that are not explicitly observed in real world diffusions. Connection strengths in the network and diffusion paths of infections over the network are examples of such hidden variables. These hidden variables therefore need to be estimated for these properties to be evaluated. In this paper, we propose and study this novel problem in a Bayesian framework by capturing the posterior distribution of these hidden variables given the observed cascades, and computing the expectation of these properties under this posterior distribution. We identify and characterize interesting network diffusion properties whose expectations can be computed exactly and efficiently, either wholly or in part. For properties that are not `nice' in this sense, we propose a Gibbs Sampling framework for Monte Carlo integration. In detailed experiments using various network diffusion properties over multiple synthetic and real datasets, we demonstrate that the proposed approach is significantly more accurate than a frequentist plug-in baseline. We also propose a map-reduce implementation of our framework and demonstrate that this can analyze cascades with millions of infections in minutes.
The battle for the future of data mining
Authors: Oren Etzioni
Venue: KDD
Year: 2014

Deep learning has catapulted to the front page of the New York Times, formed the core of the so-called 'Google brain', and achieved impressive results in vision, speech recognition, and elsewhere. Yet researchers have offered simple conundrums that deep learning doesn't address. For example, consider the sentence: 'The large ball crashed right through the table because it was made of Styrofoam.' What was made of Styrofoam? The large ball? Or the table? The answer is obviously 'the table', but if we change the word 'Styrofoam' to 'steel', the answer is clearly 'the large ball'. To automatically answer this type of question, our computers require an extensive body of knowledge. We believe that text mining can provide the requisite body of knowledge. My talk will describe work at the new Allen Institute for AI towards building the next-generation of text-mining systems.
Fast DTT: a near linear algorithm for decomposing a tensor into factor tensors
Authors: Xiaomin Fang, Rong Pan
Venue: KDD
Year: 2014

As tensors provide a natural representation for the higher-order relations, tensor factorization techniques such as Tucker decomposition and CANDECOMP/PARAFAC decomposition have been applied to many fields. Tucker decomposition has strong capacity of expression, but the time complexity is unpractical for the large-scale real problems. On the other hand, CANDECOMP/PARAFAC decomposition is linear in the feature dimensionality, but the assumption is so strong that it abandons some important information. Besides, both of TD and CP decompose a tensor into several factor matrices. However, the factor matrices are not natural for the representation of the higher-order relations. To overcome these problems, we propose a near linear tensor factorization approach, which decompose a tensor into factor tensors in order to model the higher-order relations, without loss of important information. In addition, to reduce the time complexity and the number of the parameters, we decompose each slice of the factor tensors into two smaller matrices. We conduct experiments on both synthetic datasets and real datasets. The experimental results on the synthetic datasets validate that our model has strong capacity of expression. The results on the real datasets show that our approach outperforms the state-of-the-art tensor factorization methods.
Management and analytic of biomedical big data with cloud-based in-memory database and dynamic querying: a hands-on experience with real-world data
Authors: Mengling Feng, Mohammad M. Ghassemi, Thomas Brennan, John Ellenberger, Ishrar Hussain, Roger G. Mark
Venue: KDD
Year: 2014

Analyzing Biomedical Big Data (BBD) is computationally expensive due to high dimensionality and large data volume. Performance and scalability issues of traditional database management systems (DBMS) often limit the usage of more sophisticated and complex data queries and analytic models. Moreover, in the conventional setting, data management and analysis use separate software platforms. Exporting and importing large amounts of data across platforms require a significant amount of computational and I/O resources, as well as potentially putting sensitive data at a security risk. In this tutorial, the participants will learn the difference between in-memory DBMS and traditional DBMS through hands-on exercises using SAP's cloud-based HANA in-memory DBMS in conjunction with the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) dataset. MIMIC is an open-access critical care EHR archive (over 4TB in size) and consists of structured, unstructured and waveform data. Furthermore, this tutorial will seek to educate the participants on how a combination of dynamic querying, and in-memory DBMS may enhance the management and analysis of complex clinical data.
Profit-maximizing cluster hires
Authors: Behzad Golshan, Theodoros Lappas, Evimaria Terzi
Venue: KDD
Year: 2014

Team formation has been long recognized as a natural way to acquire a diverse pool of useful skills, by combining experts with complementary talents. This allows organizations to effectively complete beneficial projects from different domains, while also helping individual experts position themselves and succeed in highly competitive job markets. Here, we assume a collection of projects ensuremath{P}, where each project requires a certain set of skills, and yields a different benefit upon completion. We are further presented with a pool of experts ensuremath{X}, where each expert has his own skillset and compensation demands. Then, we study the problem of hiring a cluster of experts T ⊆ X, so that the overall compensation (cost) does not exceed a given budget B, and the total benefit of the projects that this team can collectively cover is maximized. We refer to this as the ClusterHire problem. Our work presents a detailed analysis of the computational complexity and hardness of approximation of the problem, as well as heuristic, yet effective, algorithms for solving it in practice. We demonstrate the efficacy of our approaches through experiments on real datasets of experts, and demonstrate their advantage over intuitive baselines. We also explore additional variants of the fundamental problem formulation, in order to account for constraints and considerations that emerge in realistic cluster-hiring scenarios. All variants considered in this paper have immediate applications in the cluster hiring process, as it emerges in the context of different organizational settings.
Efficient multi-task feature learning with calibration
Authors: Pinghua Gong, Jiayu Zhou, Wei Fan, Jieping Ye
Venue: KDD
Year: 2014

Multi-task feature learning has been proposed to improve the generalization performance by learning the shared features among multiple related tasks and it has been successfully applied to many real-world problems in machine learning, data mining, computer vision and bioinformatics. Most existing multi-task feature learning models simply assume a common noise level for all tasks, which may not be the case in real applications. Recently, a Calibrated Multivariate Regression (CMR) model has been proposed, which calibrates different tasks with respect to their noise levels and achieves superior prediction performance over the non-calibrated one. A major challenge is how to solve the CMR model efficiently as it is formulated as a composite optimization problem consisting of two non-smooth terms. In this paper, we propose a variant of the calibrated multi-task feature learning formulation by including a squared norm regularizer. We show that the dual problem of the proposed formulation is a smooth optimization problem with a piecewise sphere constraint. The simplicity of the dual problem enables us to develop fast dual optimization algorithms with low per-iteration cost. We also provide a detailed convergence analysis for the proposed dual optimization algorithm. Empirical studies demonstrate that, the dual optimization algorithm quickly converges and it is much more efficient than the primal optimization algorithm. Moreover, the calibrated multi-task feature learning algorithms with and without the squared norm regularizer achieve similar prediction performance and both outperform the non-calibrated ones. Thus, the proposed variant not only enables us to develop fast optimization algorithms, but also keeps the superior prediction performance of the calibrated multi-task feature learning over the non-calibrated one.