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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PAC-Bayesian Theory Meets Bayesian Inference · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1605.08636/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1605.08636/feed.xml" rel="self" type="application/rss+xml" /><item><title>PAC-Bayesian Theory Meets Bayesian Inference — mentioned by hodgehog11</title><link>https://news.ycombinator.com/item?id=49715055</link><guid isPermaLink="false">urn:hackerpapers:mention:1605.08636:49715055</guid><pubDate>Tue, 15 Sep 2026 16:34:25 GMT</pubDate><description>&lt;p&gt;Just to add on top of the quality reference provided by srean, I like to first drill in Bayesian principles and then use this article to derive PAC-Bayes from that: https://arxiv.org/abs/1605.08636 Regular PAC falls out by taking a uniform prior over a finite hypothesis class (and then building up VC dimension if desired, but usually by this point you realise why the bounds are unlikely to be good).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1605.08636/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
