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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Detecting Errors in a Numerical Response via any Regression Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.16583/</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/2305.16583/feed.xml" rel="self" type="application/rss+xml" /><item><title>Detecting Errors in a Numerical Response via any Regression Model — mentioned by cmauck10</title><link>https://news.ycombinator.com/item?id=37558469</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.16583:37558469</guid><pubDate>Mon, 18 Sep 2023 16:34:16 GMT</pubDate><description>&lt;p&gt;Years ago, we showed the world it was possible to automatically detect label errors in classification datasets via machine learning. Since that moment, folks have asked whether the same is possible for regression datasets? Figuring out this question required extensive research since properly accounting for uncertainty (critical to decide when to trust machine learning predictions over the data itself) poses unique challenges in the regression setting. Today we have published a new paper introducing an effective method for “Detecting Errors in Numerical Data via any Regression Model”. Our…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.16583/"&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>
