Yes, but did it work? Evaluating variational inference

Yuling Yao*, Aki Vehtari, Daniel Simpson, Andrew Gelman

*Corresponding author for this work

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

10 Citations (Scopus)
34 Downloads (Pure)

Abstract

While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation". We propose two diagnostic algorithms to alleviate this problem. The Paretosmoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulationbased calibration (VSBC) assesses the average performance of point estimates.

Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
Pages8887-8895
Number of pages9
Volume12
ISBN (Electronic)9781510867963
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018
Conference number: 35

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Number80
ISSN (Electronic)1938-7228

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
CountrySweden
CityStockholm
Period10/07/201815/07/2018

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