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Analysis of Rank Data: Confluence of Social Choice, Operations Research, and Machine Learning
Shivani Agarwal · Hossein Azari Soufiani · Guy Bresler · Sewoong Oh · David Parkes · Arun Rajkumar · Devavrat Shah

Sat Dec 13 05:30 AM -- 03:30 PM (PST) @ Level 5; room 513 c,d
Event URL: http://events.csa.iisc.ernet.in/NIPS-14-rankingsws/ »

The mathematical analysis and understanding of rank data has been a fascinating topic for centuries, and has been investigated in disciplines as wide-ranging as social choice/voting theory, decision theory, probability, statistics, and combinatorics. In modern times, huge amounts of data are generated in the form of rankings on a daily basis: restaurant ratings, product ratings/comparisons, employer ratings, hospital rankings, doctor rankings, and an endless variety of rankings from committee deliberations (including, for example, deliberations of conference program committees such as NIPS!). These applications have led to several new trends and challenges: for example, one must frequently deal with very large numbers of candidates/alternatives to be ranked, with partial or missing ranking information, with noisy ranking information, with the need to ensure reliability and/or privacy of the rank data provided, and so on.

Given the increasing universality of settings involving large amounts of rank data and associated challenges as above, powerful computational frameworks and tools for addressing such challenges have emerged over the last few years in a variety of areas, including in particular in machine learning, operations research, and computational social choice. Despite the fact that many important practical problems in each area could benefit from the algorithmic solutions and analysis techniques developed in other areas, there has been limited interaction between these areas. Given both the increasing maturity of the research into ranking in these respective areas and the increasing range of practical ranking problems in need of better solutions, it is the aim of this workshop to bring together recent advances in analyzing rank data in machine learning, operations research, and computational social choice under one umbrella, to enable greater interaction and cross-fertilization of ideas.

A primary goal will be to discover connections between recent approaches developed for analyzing rank data in each of the three areas above. To this end, we will have invited talks by leading experts in the analysis of rank data in each area. In addition, we will include perspectives from practitioners who work with rank data in various applied domains on both the benefits and limitations of currently available solutions to the problems they encounter. In the end, we hope to both develop a shared language for the analysis and understanding of rank data in modern times, and identify important challenges that persist and could benefit from a shared understanding.

The topics of interest include:
- discrete choice modeling and revenue management
- voting and social decision making, preference elicitation
- social choice (rank aggregation) versus individual choice (recommendation systems)
- stochastic versus active sampling of preferences
- statistical/learning-theoretic guarantees
- effects of computational approximations

Author Information

Shivani Agarwal (University of Pennsylvania)
Hossein Azari Soufiani (Harvard University)
Guy Bresler (Massachusetts Institute of Technology)
Sewoong Oh (UIUC)
David Parkes (Harvard University)

David C. Parkes is Gordon McKay Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He was the recipient of the NSF Career Award, the Alfred P. Sloan Fellowship, the Thouron Scholarship and the Harvard University Roslyn Abramson Award for Teaching. Parkes received his Ph.D. degree in Computer and Information Science from the University of Pennsylvania in 2001, and an M.Eng. (First class) in Engineering and Computing Science from Oxford University in 1995. At Harvard, Parkes leads the EconCS group and teaches classes in artificial intelligence, optimization, and topics at the intersection between computer science and economics. Parkes has served as Program Chair of ACM EC’07 and AAMAS’08 and General Chair of ACM EC’10, served on the editorial board of Journal of Artificial Intelligence Research, and currently serves as Editor of Games and Economic Behavior and on the boards of Journal of Autonomous Agents and Multi-agent Systems and INFORMS Journal of Computing. His research interests include computational mechanism design, electronic commerce, stochastic optimization, preference elicitation, market design, bounded rationality, computational social choice, networks and incentives, multi-agent systems, crowd-sourcing and social computing.

Arun Rajkumar (Xerox Research Center, India.)
Devavrat Shah (Massachusetts Institute of Technology)

Devavrat Shah is a professor of Electrical Engineering & Computer Science and Director of Statistics and Data Science at MIT. He received PhD in Computer Science from Stanford. He received Erlang Prize from Applied Probability Society of INFORMS in 2010 and NeuIPS best paper award in 2008.

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