When can we rank well from comparisons of O(n\log(n)) non-actively chosen pairs?

Arun Rajkumar, Shivani Agarwal
; 29th Annual Conference on Learning Theory, PMLR 49:1376-1401, 2016.

Abstract

Ranking from pairwise comparisons is a ubiquitous problem and has been studied in disciplines ranging from statistics to operations research and from theoretical computer science to machine learning. Here we consider a general setting where outcomes of pairwise comparisons between items i and j are drawn probabilistically by flipping a coin with unknown bias P_ij , and ask under what conditions on these unknown probabilities one can learn a good ranking from comparisons of only O(n\log(n)) non-actively chosen pairs. Recent work has established this is possible under the Bradley-Terry-Luce (BTL) and noisy permutation (NP) models. Here we introduce a broad family of ‘low-rank’ conditions on the probabilities P_ij under which the resulting preference matrix P has low rank under some link function, and show these conditions encompass the BTL and Thurstone classes as special cases, but are considerably more general. We then give a new algorithm called low-rank pairwise ranking (LRPR) which provably learns a good ranking from comparisons of only O(n\log(n)) randomly chosen comparisons under such low-rank models. Our algorithm and analysis make use of tools from the theory of low-rank matrix completion, and provide a new perspective on the problem of ranking from pairwise comparisons in non-active settings.

Cite this Paper


BibTeX
@InProceedings{pmlr-v49-rajkumar16, title = {When can we rank well from comparisons of $O(n\log(n))$ non-actively chosen pairs?}, author = {Arun Rajkumar and Shivani Agarwal}, booktitle = {29th Annual Conference on Learning Theory}, pages = {1376--1401}, year = {2016}, editor = {Vitaly Feldman and Alexander Rakhlin and Ohad Shamir}, volume = {49}, series = {Proceedings of Machine Learning Research}, address = {Columbia University, New York, New York, USA}, month = {23--26 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v49/rajkumar16.pdf}, url = {http://proceedings.mlr.press/v49/rajkumar16.html}, abstract = {Ranking from pairwise comparisons is a ubiquitous problem and has been studied in disciplines ranging from statistics to operations research and from theoretical computer science to machine learning. Here we consider a general setting where outcomes of pairwise comparisons between items i and j are drawn probabilistically by flipping a coin with unknown bias P_ij , and ask under what conditions on these unknown probabilities one can learn a good ranking from comparisons of only O(n\log(n)) non-actively chosen pairs. Recent work has established this is possible under the Bradley-Terry-Luce (BTL) and noisy permutation (NP) models. Here we introduce a broad family of ‘low-rank’ conditions on the probabilities P_ij under which the resulting preference matrix P has low rank under some link function, and show these conditions encompass the BTL and Thurstone classes as special cases, but are considerably more general. We then give a new algorithm called low-rank pairwise ranking (LRPR) which provably learns a good ranking from comparisons of only O(n\log(n)) randomly chosen comparisons under such low-rank models. Our algorithm and analysis make use of tools from the theory of low-rank matrix completion, and provide a new perspective on the problem of ranking from pairwise comparisons in non-active settings.} }
Endnote
%0 Conference Paper %T When can we rank well from comparisons of O(n\log(n)) non-actively chosen pairs? %A Arun Rajkumar %A Shivani Agarwal %B 29th Annual Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2016 %E Vitaly Feldman %E Alexander Rakhlin %E Ohad Shamir %F pmlr-v49-rajkumar16 %I PMLR %J Proceedings of Machine Learning Research %P 1376--1401 %U http://proceedings.mlr.press %V 49 %W PMLR %X Ranking from pairwise comparisons is a ubiquitous problem and has been studied in disciplines ranging from statistics to operations research and from theoretical computer science to machine learning. Here we consider a general setting where outcomes of pairwise comparisons between items i and j are drawn probabilistically by flipping a coin with unknown bias P_ij , and ask under what conditions on these unknown probabilities one can learn a good ranking from comparisons of only O(n\log(n)) non-actively chosen pairs. Recent work has established this is possible under the Bradley-Terry-Luce (BTL) and noisy permutation (NP) models. Here we introduce a broad family of ‘low-rank’ conditions on the probabilities P_ij under which the resulting preference matrix P has low rank under some link function, and show these conditions encompass the BTL and Thurstone classes as special cases, but are considerably more general. We then give a new algorithm called low-rank pairwise ranking (LRPR) which provably learns a good ranking from comparisons of only O(n\log(n)) randomly chosen comparisons under such low-rank models. Our algorithm and analysis make use of tools from the theory of low-rank matrix completion, and provide a new perspective on the problem of ranking from pairwise comparisons in non-active settings.
RIS
TY - CPAPER TI - When can we rank well from comparisons of O(n\log(n)) non-actively chosen pairs? AU - Arun Rajkumar AU - Shivani Agarwal BT - 29th Annual Conference on Learning Theory PY - 2016/06/06 DA - 2016/06/06 ED - Vitaly Feldman ED - Alexander Rakhlin ED - Ohad Shamir ID - pmlr-v49-rajkumar16 PB - PMLR SP - 1376 DP - PMLR EP - 1401 L1 - http://proceedings.mlr.press/v49/rajkumar16.pdf UR - http://proceedings.mlr.press/v49/rajkumar16.html AB - Ranking from pairwise comparisons is a ubiquitous problem and has been studied in disciplines ranging from statistics to operations research and from theoretical computer science to machine learning. Here we consider a general setting where outcomes of pairwise comparisons between items i and j are drawn probabilistically by flipping a coin with unknown bias P_ij , and ask under what conditions on these unknown probabilities one can learn a good ranking from comparisons of only O(n\log(n)) non-actively chosen pairs. Recent work has established this is possible under the Bradley-Terry-Luce (BTL) and noisy permutation (NP) models. Here we introduce a broad family of ‘low-rank’ conditions on the probabilities P_ij under which the resulting preference matrix P has low rank under some link function, and show these conditions encompass the BTL and Thurstone classes as special cases, but are considerably more general. We then give a new algorithm called low-rank pairwise ranking (LRPR) which provably learns a good ranking from comparisons of only O(n\log(n)) randomly chosen comparisons under such low-rank models. Our algorithm and analysis make use of tools from the theory of low-rank matrix completion, and provide a new perspective on the problem of ranking from pairwise comparisons in non-active settings. ER -
APA
Rajkumar, A. & Agarwal, S.. (2016). When can we rank well from comparisons of O(n\log(n)) non-actively chosen pairs?. 29th Annual Conference on Learning Theory, in PMLR 49:1376-1401

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