OpenRaiser/Intern-Atlas huggingface dataset:
FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning
Authors: Yashoteja Prabhu, Manik Varma
Venue: KDD
Year: 2014

The objective in extreme multi-label classification is to learn a classifier that can automatically tag a data point with the most relevant subset of labels from a large label set. Extreme multi-label classification is an important research problem since not only does it enable the tackling of applications with many labels but it also allows the reformulation of ranking problems with certain advantages over existing formulations. Our objective, in this paper, is to develop an extreme multi-label classifier that is faster to train and more accurate at prediction than the state-of-the-art Multi-label Random Forest (MLRF) algorithm [2] and the Label Partitioning for Sub-linear Ranking (LPSR) algorithm [35]. MLRF and LPSR learn a hierarchy to deal with the large number of labels but optimize task independent measures, such as the Gini index or clustering error, in order to learn the hierarchy. Our proposed FastXML algorithm achieves significantly higher accuracies by directly optimizing an nDCG based ranking loss function. We also develop an alternating minimization algorithm for efficiently optimizing the proposed formulation. Experiments reveal that FastXML can be trained on problems with more than a million labels on a standard desktop in eight hours using a single core and in an hour using multiple cores.
Large scale predictive modeling for micro-simulation of 3G air interface load
Authors: Dejan Radosavljevik, Peter van der Putten
Venue: KDD
Year: 2014

This paper outlines the approach developed together with the Radio Network Strategy & Design Department of a large European telecom operator in order to forecast the Air-Interface load in their 3G network, which is used for planning network upgrades and budgeting purposes. It is based on large scale intelligent data analysis and modeling at the level of thousands of individual radio cells resulting in 30,000 models per day. It has been embedded into a scenario simulation framework that is used by end users not experienced in data mining for studying and simulating the behavior of this complex networked system, as an example of a systematic approach to the deployment step in the KDD process. This system is already in use for two years in the country where it was developed and it is a part of a standard business process. In the last six months this national operator became a competence center for predictive modeling for micro-simulation of 3G air interface load for four other operators of the same parent company.
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
Authors: Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Paul Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris J. Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, Chang-Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Maria Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, David Mares
Venue: KDD
Year: 2014

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
Does social good justify risking personal privacy?
Authors: Raghu Ramakrishnan, Geoffrey I. Webb
Venue: KDD
Year: 2014

When data-driven improvements involve personally identifiable data, or even data that can be used to infer sensitive information about individuals, we face the dilemma that we potentially risk compromising privacy. As we see increased emphasis on using data mining to effect improvements in a range of socially beneficial activities, from improving matching of talented students to opportunities for higher education, or improving allocation of funds across competing school programs, or reducing hospitalization time following surgery, the dilemma can often be especially acute. The data involved often is personally identifiable or revealing and sensitive, and many of the institutions that must be involved in gathering and maintaining custody of the data are not equipped to adequately secure the data, raising the risk of privacy breaches. How should we approach this trade-off? Can we assess the risks? Can we control or mitigate them? Can we develop guidelines for when the risk is or is not worthwhile, and for how best to handle data in different common scenarios? Chairs Raghu Ramakrishnan and Geoffrey I. Webb bring this panel of leading data miners and privacy experts together to address these critical issues.
ClusCite: effective citation recommendation by information network-based clustering
Authors: Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han
Venue: KDD
Year: 2014

Citation recommendation is an interesting but challenging research problem. Most existing studies assume that all papers adopt the same criterion and follow the same behavioral pattern in deciding relevance and authority of a paper. However, in reality, papers have distinct citation behavioral patterns when looking for different references, depending on paper content, authors and target venues. In this study, we investigate the problem in the context of heterogeneous bibliographic networks and propose a novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network. Therefore, we predict each query's citations based on related interest groups, each having its own model for paper authority and relevance. Specifically, we learn group memberships for objects and the significance of relevance features for each interest group, while also propagating relative authority between objects, by solving a joint optimization problem. Experiments on both DBLP and PubMed datasets demonstrate the power of the proposed approach, with 17.68% improvement in Recall@50 and 9.57% growth in MRR over the best performing baseline.
Dual beta process priors for latent cluster discovery in chronic obstructive pulmonary disease
Authors: James C. Ross, Peter J. Castaldi, Michael H. Cho, Jennifer G. Dy
Venue: KDD
Year: 2014

Chronic obstructive pulmonary disease (COPD) is a lung disease characterized by airflow limitation usually associated with an inflammatory response to noxious particles, such as cigarette smoke. COPD is currently the third leading cause of death in the United States and is the only leading cause of death that is increasing in prevalence. It also represents an enormous financial burden to society, costing tens of billions of dollars annually in the U.S. It is widely accepted by the medical community that COPD is a heterogeneous disease, with substantial evidence indicating that genetic variation contributes to varying levels of disease susceptibility. This heterogeneity makes it difficult to predict health decline and develop targeted treatments for better patient care. Although researchers have made several attempts to discover disease subtypes, results have been inconclusive, in part because standard clustering methods have not properly dealt with disease manifestations that may worsen with increased exposure. In this paper we introduce a transformative way of looking at the COPD subtyping task. Specifically, we model the relationship between risk factors (such as age and smoke exposure) and manifestations of disease severity using Gaussian Processes, which allow us to represent so-called "disease trajectories". We also posit that individuals can be associated with multiple disease types (latent clusters), which we assume are influenced by genetics. Furthermore, we predict that only subsets of the numerous disease-related quantitative features are useful for describing each latent subtype. We model these associations using two separate beta process priors, and we describe a variational inference approach to discover the most probable latent cluster assignments. Results are validated with associations to genetic markers.
Algorithms for interpretable machine learning
Authors: Cynthia Rudin
Venue: KDD
Year: 2014

It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction.
Deep learning
Authors: Ruslan Salakhutdinov
Venue: KDD
Year: 2014

Building intelligent systems that are capable of extracting high-level representations from high-dimensional data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep architectures that involve many layers of nonlinear processing. Many existing learning algorithms use shallow architectures, including neural networks with only one hidden layer, support vector machines, kernel logistic regression, and many others. The internal representations learned by such systems are necessarily simple and are incapable of extracting some types of complex structure from high-dimensional input. In the past few years, researchers across many different communities, from applied statistics to engineering, computer science, and neuroscience, have proposed several deep (hierarchical) models that are capable of extracting meaningful, high-level representations. An important property of these models is that they can extract complex statistical dependencies from data and efficiently learn high-level representations by re-using and combining intermediate concepts, allowing these models to generalize well across a wide variety of tasks. The learned high-level representations have been shown to give state-of-the-art results in many challenging learning problems and have been successfully applied in a wide variety of application domains, including visual object recognition, information retrieval, natural language processing, and speech perception. A few notable examples of such models include Deep Belief Networks, Deep Boltzmann Machines, Deep Autoencoders, and sparse coding-based methods. The goal of the tutorial is to introduce the recent developments of various deep learning methods to the KDD community. The core focus will be placed on algorithms that can learn multi-layer hierarchies of representations, emphasizing their applications in information retrieval, object recognition, and speech perception.
A data driven approach to diagnosing and treating disease
Authors: Eric E. Schadt
Venue: KDD
Year: 2014

Throughout the biomedical and life sciences research community, advanced integrative biology algorithms are employed to integrate large scale data across many different high-dimensional datatypes to construct predictive network models of disease. The causal inference approaches we employ for this purpose well complement the types of natural artificial intelligence/machine learning approaches that have become nearly standard in the life and biomedical sciences for building classifiers for a range of problems, from disease classification and subtype stratification, to the identification of responders and non-responders for a given treatment strategy. By building a causal network model that spans multiple scales (from the molecular to the cellular, to the tissue/organ, to the organism and community) we can understand the flow of information and how best to modulate that flow to improve human wellbeing, whether better diagnosing and treating disease or improving overall health(1-4). More specifically, we have constructed predictive network models for Alzheimer's disease, along with other common human diseases such as obesity, diabetes, heart disease, and inflammatory bowel disease, and cancer, and demonstrated a causal network common across all of these diseases(3, 5-10). Not only do we demonstrate that our predictive models uncover important mechanisms of disease and mechanistic connections among different diseases, but that they have led to a natural way to prioritize therapeutic points of intervention and provide optimal molecular phenotypes for high throughput screening. Our application of these models in a number of disease areas has led to the identification of novel genes that are causal for disease and that may serve as efficacious points of therapeutic intervention, as well as to personalized treatment strategies that provide a more quantitative and accurate approach to tailoring treatments to specific forms of disease.
Medicine in the age of electronic health records
Authors: Nigam Shah
Venue: KDD
Year: 2014

In the era of EHRs, it is possible to examine the outcomes of decisions made by doctors during clinical practice to identify patterns of care---generating evidence based on the collective practice of experts. We will discuss methods that use unstructured patient data to monitor for adverse drug events, profile specific drugs, identify off-label drug usage, uncover 'natural experiments' and generate practice-based evidence for difficult-to-test clinical hypotheses. We will describe how to detect associations among drugs and their adverse events several years before an alert is issued as well as compute the true rate of drug-drug interactions. We will present approaches to identify novel off-label uses of drugs using the patient feature matrix along with prior knowledge about drugs, diseases, and known usage. We will review a natural experiment--where a subset of congestive heart failure patients who were prescribed Cilostazol despite its black box warning--and profile its safety. We will discuss the testing of a clinical hypothesis about an association between allergic conditions and chronic uveitis in patients with juvenile idiopathic arthritis.
Reducing gang violence through network influence based targeting of social programs
Authors: Paulo Shakarian, Joseph Salmento, William R. Pulleyblank, John Bertetto
Venue: KDD
Year: 2014

In this paper, we study a variant of the social network maximum influence problem and its application to intelligently approaching individual gang members with incentives to leave a gang. The goal is to identify individuals who when influenced to leave gangs will propagate this action. We study this emerging application by exploring specific facets of the problem that must be addressed when modeling this particular situation. We formulate a new influence maximization variant - the "social incentive influence" (SII) problem and study it both formally and in the context of the law-enforcement domain. Using new techniques from unconstrained submodular maximization, we develop an approximation algorithm for SII and present a suite of experimental results - including tests on real-world police data from Chicago.
Inferring gas consumption and pollution emission of vehicles throughout a city
Authors: Jingbo Shang, Yu Zheng, Wenzhu Tong, Eric Chang, Yong Yu
Venue: KDD
Year: 2014

This paper instantly infers the gas consumption and pollution emission of vehicles traveling on a city's road network in a current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge can be used to suggest cost-efficient driving routes as well as identifying road segments where gas has been wasted significantly. The instant estimation of the emissions from vehicles can enable pollution alerts and help diagnose the root cause of air pollution in the long run. In our method, we first compute the travel speed of each road segment using the GPS trajectories received recently. As many road segments are not traversed by trajectories (i.e., data sparsity), we propose a Travel Speed Estimation (TSE) model based on a context-aware matrix factorization approach. TSE leverages features learned from other data sources, e.g., map data and historical trajectories, to deal with the data sparsity problem. We then propose a Traffic Volume Inference (TVI) model to infer the number of vehicles passing each road segment per minute. TVI is an unsupervised Bayesian Network that incorporates multiple factors, such as travel speed, weather conditions and geographical features of a road. Given the travel speed and traffic volume of a road segment, gas consumption and emissions can be calculated based on existing environmental theories. We evaluate our method based on extensive experiments using GPS trajectories generated by over 32,000 taxis in Beijing over a period of two months. The results demonstrate the advantages of our method over baselines, validating the contribution of its components and finding interesting discoveries for the benefit of society.
Prototype-based learning on concept-drifting data streams
Authors: Junming Shao, Zahra Ahmadi, Stefan Kramer
Venue: KDD
Year: 2014

Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drift in data streams, PCA and statistics based heuristic approaches are employed. SyncStream has several attractive benefits: (a) It is capable of dynamically modeling evolving concepts from even a small set of prototypes and is robust against noisy examples. (b) Owing to synchronization-based constrained clustering and the P-Tree, it supports an efficient and effective data representation and maintenance. (c) Gradual and abrupt concept drift can be effectively detected. Empirical results shows that our method achieves good predictive performance compared to state-of-the-art algorithms and that it requires much less time than another instance-based stream mining algorithm.
Log-based predictive maintenance
Authors: Ruben Sipos, Dmitriy Fradkin, Fabian Mörchen, Zhuang Wang
Venue: KDD
Year: 2014

Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures.
LASTA: large scale topic assignment on multiple social networks
Authors: Nemanja Spasojevic, Jinyun Yan, Adithya Rao, Prantik Bhattacharyya
Venue: KDD
Year: 2014

Millions of people use social networks everyday to talk about a variety of subjects, publish opinions and share information. Understanding this data to infer user's topical interests is a challenging problem with applications in various data-powered products. In this paper, we present 'LASTA' (Large Scale Topic Assignment), a full production system used at Klout, Inc., which mines topical interests from five social networks and assigns over 10,000 topics to hundreds of millions of users on a daily basis. The system continuously collects streams of user data and is reactive to fresh information, updating topics for users as interests shift. LASTA generates over 50 distinct features derived from signals such as user generated posts and profiles, user reactions such as comments and retweets, user attributions such as lists, tags and endorsements, as well as signals based on social graph connections. We show that using this diverse set of features leads to a better representation of a user's topical interests as compared to using only generated text or only graph based features. We also show that using cross-network information for a user leads to a more complete and accurate understanding of the user's topics, as compared to using any single network. We evaluate LASTA's topic assignment system on an internal labeled corpus of 32,264 user-topic labels generated from real users.
Frontiers in E-commerce personalization
Authors: Sri Subramaniam
Venue: KDD
Year: 2014

E-commerce has largely been a 'Pull' model to date. Offline retailers have nailed discovery, delight, serendipity, and impulse purchases in person with greater success than online commerce sites. However, in an always-on, mobile-first world, companies like Groupon have the opportunity to push the frontier even further than offline retailers or comprehensive sites due to the fact that our smartphones are always with us. The challenge is to provide the right deals to the right user at the right time. That involves learning about the users and their locations, their personal preferences, and predicting which deals are likely to delight them, presenting diversity, discovery and engaging UX to gather user preferences and semantic graph approaches for user-deal matching. This presentation will give insight into how Groupon manages to grapple with these challenges via a data-driven system in order to delight and surprise customers.