We explore an active top-A" ranking problem based on pairwise comparisons that are collected possibly in a sequential manner as per our design choice. We consider two settings: (1) top-K sorting in which the goal is to recover the top-A" items in order out of n items; (2) top-K partitioning where only the set of top-A' items is desired. Under a fairly general model which subsumes as special cases various models (e.g., Strong Stochastic Transitivity model, BTL model and uniform noise model), we characterize upper bounds on the sample size required for top-A∗sorting as well as for top-A" partitioning. As a consequence, we demonstrate that active ranking can offer significant multiplicative gains in sample complexity over passive ranking. Depending on the underlying stochastic noise model, such gain varies from around logn/log log n to n2 log n/log log n. We also Present an algorithm that is applicable to both settings.
|Original language||English (US)|
|Title of host publication||34th International Conference on Machine Learning, ICML 2017|
|Publisher||International Machine Learning Society (IMLS)|
|Number of pages||12|
|State||Published - 2017|
|Event||34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia|
Duration: Aug 6 2017 → Aug 11 2017
|Name||34th International Conference on Machine Learning, ICML 2017|
|Other||34th International Conference on Machine Learning, ICML 2017|
|Period||8/6/17 → 8/11/17|
Bibliographical noteFunding Information:
The authors would like to thank the reviewers who gave useful comments. C. Suh was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2015R1C1A1A02036561).
© 2017 by the author(s).
Copyright 2018 Elsevier B.V., All rights reserved.