Groves, Matthew J. (2020) Efficient pairwise information collection for subset selection. PhD thesis, University of Warwick.

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Abstract

In this work, we consider the problems of selecting the subset of the top-k best of a set of alternatives, where the fitness of alternatives must be estimated through noisy pairwise sampling. To do this, we propose two novel active pairwise sampling methods, adapted from popular non-pairwise ranking and selection frameworks. We prove that our proposed methods have desirable asymptotic properties, and demonstrate empirically that they can perform better than current state-of-the art pairwise selection algorithms on a range of tasks. We show how our proposed methods can be integrated into the Covariance Matrix Adaptation Evolutionary Strategy, to improve fitness evaluation and optimizer performance including in the evolution of neural network based agents for playing No Limit Texas Hold’em poker. Finally, we demonstrate how parametric models can be used to help our proposed sampling algorithms exploit transitive preference structure between alternative pairs.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Set theory, Paired comparisons (Statistics)
Official Date:	2020
Dates:	Date Event 2020 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Mathematics Institute
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Branke, Jürgen, 1969-
Sponsors:	Engineering and Physical Sciences Research Council ; Association for Computing Machinery
Format of File:	pdf
Extent:	xv, 139 leaves
Language:	eng

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