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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Optimization with Unknown Constraints · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1403.5607/</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/1403.5607/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bayesian Optimization with Unknown Constraints — mentioned by jdeaton</title><link>https://news.ycombinator.com/item?id=33946509</link><guid isPermaLink="false">urn:hackerpapers:mention:1403.5607:33946509</guid><pubDate>Sun, 11 Dec 2022 19:22:44 GMT</pubDate><description>&lt;p&gt;Because of criteria 0, 1, 2 these entirely depend on the individual. However, some papers which fit the criteria for me at the time were the following: - Score-Based Generative Modeling through Stochastic Differential Equations https://arxiv.org/abs/2011.13456 - Structured Denoising Diffusion Models in Discrete State-Spaces https://arxiv.org/abs/2107.03006 - Efficient and Modular Implicit Differentiation https://arxiv.org/abs/2105.15183 - Scalable Gradients for Stochastic Differential Equations https://arxiv.org/abs/2001.01328 - Bayesian Optimization with Unknown Constraints…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1403.5607/"&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>
