Exponentiated strongly Rayleigh distributions
Name
NeurIPS-2018-exponentiated-strongly-rayleigh-distributions-Paper.pdf
Description
Published version
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516.73 KB
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Checksum (MD5)
9608e5c8c793e4e3623cc50a2f277b42
Author(s)
Mariet, Zelda
Sra, Suvrit
Jegelka, Stefanie Sabrina
Date Issued
2018
Journal
Advances in Neural Information Processing Systems
Version
Final published version
Abstract
© 2018 Curran Associates Inc..All rights reserved. Strongly Rayleigh (SR) measures are discrete probability distributions over the subsets of a ground set. They enjoy strong negative dependence properties, as a result of which they assign higher probability to subsets of diverse elements. We introduce in this paper Exponentiated Strongly Rayleigh (ESR) measures, which sharpen (or smoothen) the negative dependence property of SR measures via a single parameter (the exponent) that can be intuitively understood as an inverse temperature. We develop efficient MCMC procedures for approximate sampling from ESRs, and obtain explicit mixing time bounds for two concrete instances: exponentiated versions of Determinantal Point Processes and Dual Volume Sampling. We illustrate some of the potential of ESRs, by applying them to a few machine learning problems; empirical results confirm that beyond their theoretical appeal, ESR-based models hold significant promise for these tasks.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://papers.nips.cc/paper/2018/hash/1c6a0198177bfcc9bd93f6aab94aad3c-Abstract.html
