____________________________________________________ CFP: Analysis of Rank Data Workshop: NIPS 2014 ___________________________________________________
NIPS Workshop on Analysis of Rank Data: Confluence of Social Choice, Operations Research and Machine Learning ___________________________________________________ December 13, 2014, Montreal, Canada *http://events.csa.iisc.ernet.in/NIPS-14-rankingsws <http://events.csa.iisc.ernet.in/NIPS-14-rankingsws>* ___________________________________________________ Analysis of Rank Data: Workshop Overview ---------------------------------------------------------------- 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. Call for Papers: ---------------------- We welcome submissions to the workshop in topics of interest which include but are not limited to - discrete choice modeling and revenue management - voting and social decision making, preference elicitation - social choice (rank aggregation) - Individual choice (recommendation systems) - stochastic versus active sampling of preferences - statistical/learning-theoretic guarantees - effects of computational approximations Papers submitted to the workshop should be up to four pages long excluding references and in NIPS 2014 format. They should be sent by email to* [email protected] <[email protected]>*. Accepted submissions will be presented as short talks and/or posters. Invited Talks: ------------------- Garrett Van Ryzin (Columbia University) Craig Boutilier (University of Toronto) Guy Lebanon (Amazon) (tentative) Eyke Hullermeier (Marburg university) (tentative) Important Dates: ------------------------ Submission deadline: *October 9, 2014* Acceptance notification: October 23, 2014 Workshop: December 13, 2014 Registration: ----------------- Please refer to NIPS-2014 website http://nips.cc/Conferences/2014/ for registration details as they become available. Organizers: ----------------- Shivani Agarwal (Indian Institute of Science) Hossein Azari (Harvard) Guy Bresler (MIT) Sewoong Oh (UIUC) David Parkes (Harvard) Arun Rajkumar (Indian Institute of Science) Devavrat Shah (MIT) ___________________________________________________
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