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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Efficient Exploration in Binary and Preferential Bayesian Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2110.09361/</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/2110.09361/feed.xml" rel="self" type="application/rss+xml" /><item><title>Efficient Exploration in Binary and Preferential Bayesian Optimization — mentioned by aeytan</title><link>https://news.ycombinator.com/item?id=29223051</link><guid isPermaLink="false">urn:hackerpapers:mention:2110.09361:29223051</guid><pubDate>Mon, 15 Nov 2021 02:45:29 GMT</pubDate><description>&lt;p&gt;Were you trying to do optimization using a binary outcome? I am not familiar with many packages that do this out of the box. Your implementation looks like a good start for the binary case, but you will get better results by computing expected improvement on the probability of winning, (i.e., Phi(f(x)), rather than f(x), where Phi() is the standard normal CDF). For more on why, see http://proceedings.mlr.press/v28/tesch13.pdf and https://arxiv.org/pdf/2110.09361.pdf. AFAIK this should be as simple as defining a simple MCAcquisitionObjective (https://botorch.org/docs/objectives) that passes…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2110.09361/"&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>
