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Top-k Selection based on Adaptive Sampling of Noisy Preferences

Robert Busa-Fekete Balazs Szorenyi Paul Weng 1 Weiwei Cheng Eyke Hüllermeier
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We consider the problem of reliably selecting an optimal subset of fixed size from a given set of choice alternatives, based on noisy information about the quality of these alternatives. Problems of similar kind have been tackled by means of adaptive sampling schemes called racing algorithms. However, in contrast to existing approaches, we do not assume that each alternative is characterized by a real-valued random variable, and that samples are taken from the corresponding distributions. Instead, we only assume that alternatives can be compared in terms of pairwise preferences. We propose and formally analyze a general preference-based racing algorithm that we instantiate with three specific ranking procedures and corresponding sampling schemes. Experiments with real and synthetic data are presented to show the efficiency of our approach.
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Submitted on : Thursday, October 15, 2015 - 2:51:32 PM
Last modification on : Thursday, March 21, 2019 - 12:59:14 PM


  • HAL Id : hal-01216055, version 1


Robert Busa-Fekete, Balazs Szorenyi, Paul Weng, Weiwei Cheng, Eyke Hüllermeier. Top-k Selection based on Adaptive Sampling of Noisy Preferences. International Conference on Machine Learning, Jun 2013, Atlanta, Georgia, United States. pp.1094-1102. ⟨hal-01216055⟩



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