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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Active Learning for Classification and Preference Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1112.5745/</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/1112.5745/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bayesian Active Learning for Classification and Preference Learning — mentioned by razcle</title><link>https://news.ycombinator.com/item?id=26028063</link><guid isPermaLink="false">urn:hackerpapers:mention:1112.5745:26028063</guid><pubDate>Thu, 04 Feb 2021 18:09:31 GMT</pubDate><description>&lt;p&gt;Hi NMCA, I&amp;#x27;m Raza one of the founders of Humanloop. I totally agree that transfer learning is one of the best strategies for data efficiency and it&amp;#x27;s pretty common to see people start from large pre-trained models like BERT. Active learning then provides an additional benefit, especially when labels are expensive. For example we&amp;#x27;ve worked with teams where lawyers have to annotate. In terms of breakthroughs in recent years, some things I&amp;#x27;d point to would BALD (https://arxiv.org/abs/1112.5745) and its applications in deep learning as well. There has also been progress in coreset methods…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1112.5745/"&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>
