Fixed-point algorithms for learning determinantal point processes

Zelda Mariet, Suvrit Sra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.

Original languageEnglish
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
PublisherInternational Machine Learning Society (IMLS)
Pages2379-2387
Number of pages9
ISBN (Electronic)9781510810587
StatePublished - 2015
Externally publishedYes
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: 6 Jul 201511 Jul 2015

Publication series

Name32nd International Conference on Machine Learning, ICML 2015
Volume3

Conference

Conference32nd International Conference on Machine Learning, ICML 2015
Country/TerritoryFrance
CityLile
Period6/07/1511/07/15

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