OpenRaiser/Intern-Atlas huggingface dataset:
Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance
Authors: Aurélie C. Lozano, Huijing Jiang, Xinwei Deng
Venue: KDD
Year: 2013

We propose a robust framework to jointly perform two key modeling tasks involving high dimensional data: (i) learning a sparse functional mapping from multiple predictors to multiple responses while taking advantage of the coupling among responses, and (ii) estimating the conditional dependency structure among responses while adjusting for their predictors. The traditional likelihood-based estimators lack resilience with respect to outliers and model misspecification. This issue is exacerbated when dealing with high dimensional noisy data. In this work, we propose instead to minimize a regularized distance criterion, which is motivated by the minimum distance functionals used in nonparametric methods for their excellent robustness properties. The proposed estimates can be obtained efficiently by leveraging a sequential quadratic programming algorithm. We provide theoretical justification such as estimation consistency for the proposed estimator. Additionally, we shed light on the robustness of our estimator through its linearization, which yields a combination of weighted lasso and graphical lasso with the sample weights providing an intuitive explanation of the robustness. We demonstrate the merits of our framework through simulation study and the analysis of real financial and genetics data.
Cyber security: how visual analytics unlock insight
Authors: Raffael Marty
Venue: KDD
Year: 2013

In the Cyber Security domain, we have been collecting 'big data' for almost two decades. The volume and variety of our data is extremely large, but understanding and capturing the semantics of the data is even more of a challenge. Finding the needle in the proverbial haystack has been attempted from many different angles. In this talk we will have a look at what approaches have been explored, what has worked, and what has not. We will see that there is still a large amount of work to be done and data mining is going to play a central role. We'll try to motivate that in order to successfully find bad guys, we will have to embrace a solution that not only leverages clever data mining, but employs the right mix between human computer interfaces, data mining, and scalable data platforms. Traditionally, cyber security has been having its challenges with data mining. We are different. We will explore how to adopt data mining algorithms to the security domain. Some approaches like predictive analytics are extremely hard, if not impossible. How would you predict the next cyber attack? Others need to be tailored to the security domain to make them work.
Long-memory time series ensembles for concept shift detection
Authors: Marcelo Mendoza, Barbara Poblete, Felipe Bravo-Marquez, Daniel Gayo-Avello
Venue: KDD
Year: 2013

Usually time series are controlled by generative processes which display changes over time. On many occasions, two or more generative processes may switch forcing the abrupt replacement of a fitted time series model by another one. We claim that the incorporation of past data can be useful in the presence of concept shift. We believe that history tends to repeat itself and from time to time, it is desirable to discard recent data reusing old past data to perform model fitting and forecasting. We address this challenge by introducing an ensemble method that deals with long-memory time series. Our method starts by segmenting historical time series data to identify data segments which present model consistency. Then, we project the time series by using data segments which are close to current data. By using a dynamic time warping alignment function, we try to anticipate concept shifts, looking for similarities between current data and the prequel of a past shift. We evaluate our proposal on non-stationary and non-linear time series. To achieve this we perform forecasting accuracy testing against well known state-of-the-art methods such as neural networks and threshold auto regressive models. Our results show that the proposed method anticipates many concept shifts.
Cross-task crowdsourcing
Authors: Kaixiang Mo, Erheng Zhong, Qiang Yang
Venue: KDD
Year: 2013

Crowdsourcing is an effective method for collecting labeled data for various data mining tasks. It is critical to ensure the veracity of the produced data because responses collected from different users may be noisy and unreliable. Previous works solve this veracity problem by estimating both the user ability and question difficulty based on the knowledge in each task individually. In this case, each single task needs large amounts of data to provide accurate estimations. However, in practice, budgets provided by customers for a given target task may be limited, and hence each question can be presented to only a few users where each user can answer only a few questions. This data sparsity problem can cause previous approaches to perform poorly due to the overfitting problem on rare data and eventually damage the data veracity. Fortunately, in real-world applications, users can answer questions from multiple historical tasks. For example, one can annotate images as well as label the sentiment of a given title. In this paper, we employ transfer learning, which borrows knowledge from auxiliary historical tasks to improve the data veracity in a given target task. The motivation is that users have stable characteristics across different crowdsourcing tasks and thus data from different tasks can be exploited collectively to estimate users' abilities in the target task. We propose a hierarchical Bayesian model, TLC (Transfer Learning for Crowdsourcing), to implement this idea by considering the overlapping users as a bridge. In addition, to avoid possible negative impact, TLC introduces task-specific factors to model task differences. The experimental results show that TLC significantly improves the accuracy over several state-of-the-art non-transfer-learning approaches under very limited budget in various labeling tasks.
Experience from hosting a corporate prediction market: benefits beyond the forecasts
Authors: Thomas A. Montgomery, Paul M. Stieg, Michael J. Cavaretta, Paul E. Moraal
Venue: KDD
Year: 2013

Prediction markets are virtual stock markets used to gain insight and forecast events by leveraging the wisdom of crowds. Popularly applied in the public to cultural questions (election results, box-office returns), they have recently been applied by corporations to leverage employee knowledge and forecast answers to business questions (sales volumes, products and features, release timing). Determining whether to run a prediction market requires practical experience that is rarely described.
Modelling political disaffection from Twitter data
Authors: Corrado Monti, Alessandro Rozza, Giovanni Zappella, Matteo Zignani, Adam Arvidsson, Elanor Colleoni
Venue: KDD
Year: 2013

Twitter is one of the most popular micro-blogging services in the world, often studied in the context of political opinion mining for its peculiar nature of online public discussion platform. In our work we analyse the phenomenon of political disaffection defined as the "lack of confidence in the political process, politicians, and democratic institutions, but with no questioning of the political regime". Disaffection for organised political parties and institutions has been object of studies and media attention in several Western countries. Especially the Italian case has shown a wide diffusion of this attitude. For this reason, we collect a massive database of Italian Twitter data (about 35 millions of tweets from April 2012 to October 2012) and we exploit scalable state-of-the-art machine learning techniques to generate time-series concerning the political disaffection discourse. In order to validate the quality of the time-series generated, we compare them with indicators of political disaffection from public opinion surveys. We find political disaffection on Twitter to be highly correlated with the indicators of political disaffection in the public opinion surveys. Moreover, we show the peaks in the timeseries are often generated by external political events reported on the main newspapers.
Learning mixed kronecker product graph models with simulated method of moments
Authors: Sebastián Moreno, Jennifer Neville, Sergey Kirshner
Venue: KDD
Year: 2013

There has recently been a great deal of work focused on developing statistical models of graph structure---with the goal of modeling probability distributions over graphs from which new, similar graphs can be generated by sampling from the estimated distributions. Although current graph models can capture several important characteristics of social network graphs (e.g., degree, path lengths), many of them do not generate graphs with sufficient variation to reflect the natural variability in real world graph domains. One exception is the mixed Kronecker Product Graph Model (mKPGM), a generalization of the Kronecker Product Graph Model, which uses parameter tying to capture variance in the underlying distribution [10]. The enhanced representation of mKPGMs enables them to match both the mean graph statistics and their spread as observed in real network populations, but unfortunately to date, the only method to estimate mKPGMs involves an exhaustive search over the parameters.
Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data
Authors: Shouichi Nagano, Yusuke Ichikawa, Noriko Takaya, Tadasu Uchiyama, Makoto Abe
Venue: KDD
Year: 2013

An important problem in the non-contractual marketing domain is discovering the customer lifetime and assessing the impact of customer's characteristic variables on the lifetime. Unfortunately, the conventional hierarchical Bayes model cannot discern the impact of customer's characteristic variables for each customer. To overcome this problem, we present a new survival model using a non-parametric Bayes paradigm with MCMC. The assumption of a conventional model, logarithm of purchase rate and dropout rate with linear regression, is extended to include our assumption of the Dirichlet Process Mixture of regression. The extension assumes that each customer belongs probabilistically to different mixtures of regression, thereby permitting us to estimate a different impact of customer characteristic variables for each customer. Our model creates several customer groups to mirror the structure of the target data set.
Using "big data" to solve "small data" problems
Authors: Chris Neumann
Venue: KDD
Year: 2013

The brief history of knowledge discovery is filled with products that promised to bring "BI to the masses". But how do you build a product that truly bridges the gap between the conceptual simplicity of "questions and answers" and the structure needed to query traditional data stores?
The online revolution: education for everyone
Authors: Andrew Y. Ng, Daphne Koller
Venue: KDD
Year: 2013

In 2011, Stanford University offered three online courses, which anyone in the world could enroll in and take for free. Together, these three courses had enrollments of around 350,000 students, making this one of the largest experiments in online education ever performed. Since the beginning of 2012, we have transitioned this effort into a new venture, Coursera, a social entrepreneurship company whose mission is to make high-quality education accessible to everyone by allowing the best universities to offer courses to everyone around the world, for free. Coursera classes provide a real course experience to students, including video content, interactive exercises with meaningful feedback, using both auto-grading and peer-grading, and a rich peer-to-peer interaction around the course materials. Currently, Coursera has 62 university partners, and over 3 million students enrolled in its over 300 courses. These courses span a range of topics including computer science, business, medicine, science, humanities, social sciences, and more. In this talk, I'll report on this far-reaching experiment in education, and why we believe this model can provide both an improved classroom experience for our on-campus students, via a flipped classroom model, as well as a meaningful learning experience for the millions of students around the world who would otherwise never have access to education of this quality.
Mining data from mobile devices: a survey of smart sensing and analytics
Authors: Spiros Papadimitriou, Tina Eliassi-Rad
Venue: KDD
Year: 2013

Mobile connected devices, and smartphones in particular, are rapidly emerging as a dominant computing and sensing platform. This poses several unique opportunities for data collection and analysis, as well as new challenges. In this tutorial, we survey the state-of-the-art in terms of mining data from mobile devices across different application areas such as ads, healthcare, geosocial, public policy, etc. Our tutorial has three parts. In part one, we summarize data collection in terms of various sensing modalities. In part two, we present cross-cutting challenges such as real-time analysis, security, and we outline cross cutting methods for mobile data mining such as network inference, streaming algorithms, etc. In the last part, we specifically overview emerging and fast-growing application areas, such as noted above. Concluding, we briefly highlight the opportunities for joint design of new data collection techniques and analysis methods, suggesting additional directions for future research.
Scalable supervised dimensionality reduction using clustering
Authors: Troy Raeder, Claudia Perlich, Brian Dalessandro, Ori Stitelman, Foster J. Provost
Venue: KDD
Year: 2013

The automated targeting of online display ads at scale requires the simultaneous evaluation of a single prospect against many independent models. When deciding which ad to show to a user, one must calculate likelihood-to-convert scores for that user across all potential advertisers in the system. For modern machine-learning-based targeting, as conducted by Media6Degrees (M6D), this can mean scoring against thousands of models in a large, sparse feature space. Dimensionality reduction within this space is useful, as it decreases scoring time and model storage requirements. To meet this need, we develop a novel algorithm for scalable supervised dimensionality reduction across hundreds of simultaneous classification tasks. The algorithm performs hierarchical clustering in the space of model parameters from historical models in order to collapse related features into a single dimension. This allows us to implicitly incorporate feature and label data across all tasks without operating directly in a massive space. We present experimental results showing that for this task our algorithm outperforms other popular dimensionality-reduction algorithms across a wide variety of ad campaigns, as well as production results that showcase its performance in practice.
Scale-out beyond map-reduce
Authors: Raghu Ramakrishnan
Venue: KDD
Year: 2013

The amount and variety of data being collected in the enterprise is growing at a staggering pace. The default now is to capture and store any and all data, in anticipation of potential future strategic value, and vast amounts of data are being generated by instrumenting key customer and systems touch points. Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of large datasets to be stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning. These new systems are necessarily based on scale-out architectures for both storage and computation. The terms Big Data and data science are often used to refer to this class of systems and applications.
Mining discriminative subgraphs from global-state networks
Authors: Sayan Ranu, Minh X. Hoang, Ambuj K. Singh
Venue: KDD
Year: 2013

Global-state networks provide a powerful mechanism to model the increasing heterogeneity in data generated by current systems. Such a network comprises of a series of network snapshots with dynamic local states at nodes, and a global network state indicating the occurrence of an event. Mining discriminative subgraphs from global-state networks allows us to identify the influential sub-networks that have maximum impact on the global state and unearth the complex relationships between the local entities of a network and their collective behavior. In this paper, we explore this problem and design a technique called MINDS to mine minimally discriminative subgraphs from large global-state networks. To combat the exponential subgraph search space, we derive the concept of an edit map and perform Metropolis Hastings sampling on it to compute the answer set. Furthermore, we formulate the idea of network-constrained decision trees to learn prediction models that adhere to the underlying network structure. Extensive experiments on real datasets demonstrate excellent accuracy in terms of prediction quality. Additionally, MINDS achieves a speed-up of at least four orders of magnitude over baseline techniques.
An online system with end-user services: mining novelty concepts from tv broadcast subtitles
Authors: Mika Rautiainen, Jouni Sarvanko, Arto Heikkinen, Mika Ylianttila, Vassilis Kostakos
Venue: KDD
Year: 2013

Better tools for content-based access of video are needed to improve access to time-continuous video data. Particularly information about linear TV broadcast programs has been available in a form limited to program guides that provide short manually described overviews of the program content. Recent development in digitalization of TV broadcasting and emergence of web-based services for catch-up and on-demand viewing bring out new possibilities to access data. In this paper we introduce our data mining system and accompanying services for summarizing Finnish DVB broadcast streams from seven national channels. We describe how data mining of novelty concepts can be extracted from DVB subtitles to augment web-based "Catch-Up TV Guide" and "Novelty Cloud" TV services. Furthermore, our system allows accessing media fragments as Picture Quotes via generated word lists and provides content-based recommendations to find new programs that have content similar to the user selected programs. Our index consists of over 180 000 programs that are used to recommend relevant programs. The service has been under development and available online since 2010. It has registered over 5000 user sessions.
A time-dependent enhanced support vector machine for time series regression
Authors: Goce Ristanoski, Wei Liu, James Bailey
Venue: KDD
Year: 2013

Support Vector Machines (SVMs) are a leading tool in machine learning and have been used with considerable success for the task of time series forecasting. However, a key challenge when using SVMs for time series is the question of how to deeply integrate time elements into the learning process. To address this challenge, we investigated the distribution of errors in the forecasts delivered by standard SVMs. Once we identified the samples that produced the largest errors, we observed their correlation with distribution shifts that occur in the time series. This motivated us to propose a time-dependent loss function which allows the inclusion of the information about the distribution shifts in the series directly into the SVM learning process. We present experimental results which indicate that using a time-dependent loss function is highly promising, reducing the overall variance of the errors, as well as delivering more accurate predictions.
Direct out-of-memory distributed parallel frequent pattern mining
Authors: Zheyi Rong, Jeroen De Knijf
Venue: KDD
Year: 2013

Frequent itemset mining is a well studied and important problem in the datamining community. An abundance of different mining algorithms exists, all with different flavor and characteristics, but almost all suffer from two major shortcomings. First, in general frequent itemset mining algorithms perform exhaustive search over a huge pattern space. Second, most algorithms assume that the input data fits into main memory. The first problem was recently tackled in the work of [2], by direct sampling the required number of patterns over the pattern space. This paper extends the direct sampling approach by casting the algorithm into the MapReduce framework, effectively ceasing the memory requirements that the data should fit into main memory. The results show that the algorithm scales well for large data sets, while the memory requirements are solely dependent on the required number of patterns in the output.
The Microsoft academic search dataset and KDD Cup 2013
Authors: Senjuti Basu Roy, Martine De Cock, Vani Mandava, Swapna Savanna, Brian Dalessandro, Claudia Perlich, William Cukierski, Ben Hamner
Venue: KDD
Year: 2013

KDD Cup 2013 challenged participants to tackle the problem of author name ambiguity in a digital library of scientific publications. The competition consisted of two tracks, which were based on large-scale datasets from a snapshot of Microsoft Academic Search, taken in January 2013 and including 250K authors and 2.5M papers. Participants were asked to determine which papers in an author profile are truly written by a given author (track 1), as well as to identify duplicate author profiles (track 2). Track 1 and track 2 were launched respectively on April 18 and April 20, 2013, with a common final submission deadline on June 12, 2013. For track 1 a training dataset with correct labels was diclosed at the start of the competition. This track was the most popular one, attracting submissions of 561 different teams. Track 2, which was formulated as an unsupervised learning task, received submissions from 241 participants. This paper presents details about the problem definitions, the datasets, the evaluation metrics and the results.
Mining the digital universe of data to develop personalized cancer therapies
Authors: Eric E. Schadt
Venue: KDD
Year: 2013

The development of a personalized approach to medical care is now well recognized as an urgent priority. This approach is particularly important in oncology, where it is well understood that each cancer diagnosis is unique at the molecular level, arising from a particular and specific collection of genetic alterations. Furthermore, taking a personalized approach to oncology may expedite the treatment process, pre-empting therapeutic decisions based on fewer data in favor of treatments targeted to an individual's tumor. This directed course may be key to survival for many patients who are terminal or have failed standard therapies.
KDD Cup 2013: author disambiguation
Authors: Benjamin Solecki, Lucas Silva, Dmitry Efimov
Venue: KDD
Year: 2013

This paper describes our team's (BS Man & Dmitry & Leustagos) approach to the KDD Cup 2013 track 2 challenge: Author Disambiguation in the Microsoft Academic Search database.