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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Freeze-Thaw Bayesian Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1406.3896/</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/1406.3896/feed.xml" rel="self" type="application/rss+xml" /><item><title>Freeze-Thaw Bayesian Optimization — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=47448110</link><guid isPermaLink="false">urn:hackerpapers:mention:1406.3896:47448110</guid><pubDate>Thu, 19 Mar 2026 23:43:50 GMT</pubDate><description>&lt;p&gt;Yes, it&amp;#x27;s greedy so may hit local optima. You can fit learning curves and try to extrapolate out to avoid that problem, to let you run long enough to be reasonably sure of a dead end, and periodically revive past candidates to run longer. See past hyperparameter approaches like freeze-thaw https://arxiv.org/abs/1406.3896 .&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1406.3896/"&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>
