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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Yes, but Did It Work?: Evaluating Variational Inference · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1802.02538/</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/1802.02538/feed.xml" rel="self" type="application/rss+xml" /><item><title>Yes, but Did It Work?: Evaluating Variational Inference — mentioned by cfcf14</title><link>https://news.ycombinator.com/item?id=34934967</link><guid isPermaLink="false">urn:hackerpapers:mention:1802.02538:34934967</guid><pubDate>Sat, 25 Feb 2023 08:32:17 GMT</pubDate><description>&lt;p&gt;Amazing post, agree with everything you&amp;#x27;ve said. I&amp;#x27;ve always felt that the problems with advanced MCMC methods (HMC, RM-MC, etc) are even more painful when one looks at approximate bayesian methods - ADVI (variational approximation), SGLD (langevin dynamics), and so on. My grad research was on ABC-SMC methods, probably the last resort of all last resorts. There was a period a few years ago when it was all the rage to take arbitrary probabilistic programming models and just toss ADVI at it blindly with fancy tools like Pymc3 or stan. I feel like everybody eventually came to the conclusion that…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1802.02538/"&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>
