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Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood

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Bibliographic Details
Published in:Journal of Machine Learning Research (JMLR)
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Machine Learning Research (JMLR)
description
discipline_display Computer Sciience
discipline_facet Computer Sciience
format Online Article
RSS Article
genre Journal Article
id rss_article:77724
institution FRELIP
journal_source_facet Journal of Machine Learning Research (JMLR)
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
Computer Sciience
General
Computer Sciience
sub_discipline_display General
sub_discipline_facet General
subject_display Computer Sciience
General
Computer Sciience
Computer Sciience
General
Computer Sciience
subject_facet Computer Sciience
General
Computer Sciience
title Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
title_auth Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
title_full Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
title_fullStr Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
title_full_unstemmed Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
title_short Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
title_sort bayesian inference of contextual bandit policies via empirical likelihood
topic Computer Sciience
General
Computer Sciience
url http://jmlr.org/papers/v27/23-0958.html