Timezone: »
Ranking problems are increasingly recognized as a new class of statistical learning problems that are distinct from the classical learning problems of classification and regression. Such problems arise in a wide variety of domains: in information retrieval, one wants to rank documents according to relevance to a query; in natural language processing, one wants to rank alternative parses or translations of a sentence; in collaborative filtering, one wants to rank items according to a user's likes and dislikes; in computational biology, one wants to rank genes according to relevance to a disease. Consequently, there has been much interest in ranking in recent years, with a variety of methods being developed and a whole host of new applications being discovered.
This workshop aims to bring together researchers interested in the area to share their perspectives, identify persisting challenges as well as opportunities for meaningful dialogue and collaboration, and to discuss possible directions for further advances and applications in the future.
One of the primary goals of the workshop will be to reach out to a broad audience. To this end, we will have talks on topics ranging from more statistically/mathematically oriented approaches to ranking, to newer application areas. A second goal will be to bring to the fore a range of questions that are currently being debated within the community, for example via a panel discussion between experts in the field.
Overall, the workshop will aim to provide a forum that showcases recent advances in ranking to the broader community, facilitates open debate on some of the questions in this area, and helps catalyze further interest among those new to the topic.
Author Information
Shivani Agarwal (University of Pennsylvania)
Chris J Burges (Microsoft Research)
Yacov Crammer (Technion)
More from the Same Authors
-
2022 Poster: Finite Sample Analysis Of Dynamic Regression Parameter Learning »
Mark Kozdoba · Edward Moroshko · Shie Mannor · Yacov Crammer -
2018 Poster: Efficient Loss-Based Decoding on Graphs for Extreme Classification »
Itay Evron · Edward Moroshko · Yacov Crammer -
2017 Poster: Rotting Bandits »
Nir Levine · Yacov Crammer · Shie Mannor -
2016 Poster: Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions »
Siddartha Ramamohan · Arun Rajkumar · Shivani Agarwal · Shivani Agarwal -
2015 Poster: Linear Multi-Resource Allocation with Semi-Bandit Feedback »
Tor Lattimore · Yacov Crammer · Csaba Szepesvari -
2014 Workshop: 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 -
2014 Workshop: Learning Semantics »
Cedric Archambeau · Antoine Bordes · Leon Bottou · Chris J Burges · David Grangier -
2014 Poster: Learning Multiple Tasks in Parallel with a Shared Annotator »
Haim Cohen · Yacov Crammer -
2014 Poster: On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures »
Harikrishna Narasimhan · Rohit Vaish · Shivani Agarwal -
2014 Poster: Online Decision-Making in General Combinatorial Spaces »
Arun Rajkumar · Shivani Agarwal -
2013 Workshop: Resource-Efficient Machine Learning »
Yevgeny Seldin · Yasin Abbasi Yadkori · Yacov Crammer · Ralf Herbrich · Peter Bartlett -
2013 Poster: Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses »
Harish G Ramaswamy · Shivani Agarwal · Ambuj Tewari -
2013 Poster: On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation »
Harikrishna Narasimhan · Shivani Agarwal -
2013 Spotlight: On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation »
Harikrishna Narasimhan · Shivani Agarwal -
2013 Spotlight: Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses »
Harish G Ramaswamy · Shivani Agarwal · Ambuj Tewari -
2013 Session: Oral Session 7 »
Chris J Burges -
2012 Workshop: Multi-Trade-offs in Machine Learning »
Yevgeny Seldin · Guy Lever · John Shawe-Taylor · Nicolò Cesa-Bianchi · Yacov Crammer · Francois Laviolette · Gabor Lugosi · Peter Bartlett -
2012 Poster: Classification Calibration Dimension for General Multiclass Losses »
Harish G Ramaswamy · Shivani Agarwal -
2012 Spotlight: Classification Calibration Dimension for General Multiclass Losses »
Harish G Ramaswamy · Shivani Agarwal -
2012 Poster: Volume Regularization for Binary Classification »
Yacov Crammer · Tal Wagner -
2012 Spotlight: Volume Regularization for Binary Classification »
Yacov Crammer · Tal Wagner -
2012 Poster: Learning Multiple Tasks using Shared Hypotheses »
Yacov Crammer · Yishay Mansour -
2011 Workshop: New Frontiers in Model Order Selection »
Yevgeny Seldin · Yacov Crammer · Nicolò Cesa-Bianchi · Francois Laviolette · John Shawe-Taylor -
2010 Poster: Learning via Gaussian Herding »
Yacov Crammer · Daniel Lee -
2010 Poster: New Adaptive Algorithms for Online Classification »
Francesco Orabona · Yacov Crammer -
2009 Poster: Adaptive Regularization of Weight Vectors »
Yacov Crammer · Alex Kulesza · Mark Dredze -
2009 Spotlight: Adaptive Regularization of Weight Vectors »
Yacov Crammer · Alex Kulesza · Mark Dredze -
2008 Session: Oral session 6: Neural Coding »
Yacov Crammer -
2008 Poster: Exact Convex Confidence-Weighted Learning »
Yacov Crammer · Mark Dredze · Fernando Pereira -
2008 Spotlight: Exact Convex Confidence-Weighted Learning »
Yacov Crammer · Mark Dredze · Fernando Pereira -
2007 Spotlight: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting »
Ping Li · Chris J Burges · Qiang Wu -
2007 Poster: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting »
Ping Li · Chris J Burges · Qiang Wu -
2007 Poster: Learning Bounds for Domain Adaptation »
John Blitzer · Yacov Crammer · Alex Kulesza · Fernando Pereira · Jennifer Wortman Vaughan -
2006 Poster: Learning from Multiple Sources »
Yacov Crammer · Michael Kearns · Jennifer Wortman Vaughan -
2006 Poster: Analysis of Representations for Domain Adaptation »
John Blitzer · Shai Ben-David · Yacov Crammer · Fernando Pereira -
2006 Poster: Learning to Rank with Nonsmooth Cost Functions »
Chris J Burges · Quoc Le · Robert J Ragno