Correcting Predictions for Approximate Bayesian Inference

Tomasz Kusmierczyk, Joseph Sakaya, Arto Klami

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


Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.
Original languageEnglish
Title of host publicationThe thirty-fourth AAAI conference on artificial intelligence
Number of pages8
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Publication dateFeb 2020
ISBN (Electronic)978-1-57735-835-0
Publication statusPublished - Feb 2020
MoE publication typeA4 Article in conference proceedings
Event34th AAAI Conference on Artificial Intelligence (AAAI-20) - New York, United States
Duration: 7 Feb 202012 Feb 2020

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Fields of Science

  • 113 Computer and information sciences

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