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The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks

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Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Machine Learning Research
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
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genre Journal Article
id rss_article:9437
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
title_auth The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
title_full The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
title_fullStr The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
title_full_unstemmed The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
title_short The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
title_sort the surrogate gibbs-posterior of a corrected stochastic mala: towards uncertainty quantification for neural networks
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v27/22-0483.html