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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Banzhaf: A Robust Data Valuation Framework for Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2205.15466/</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/2205.15466/feed.xml" rel="self" type="application/rss+xml" /><item><title>Data Banzhaf: A Robust Data Valuation Framework for Machine Learning — mentioned by nickvincent</title><link>https://news.ycombinator.com/item?id=34673150</link><guid isPermaLink="false">urn:hackerpapers:mention:2205.15466:34673150</guid><pubDate>Mon, 06 Feb 2023 03:55:46 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s an open challenge, this preprint proposes a solution for robust data valuation. https://arxiv.org/abs/2205.15466 However, I&amp;#x27;m not aware of anyone actively trying to adapt these techniques for large generative AI models (though would be great to see).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2205.15466/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
