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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Human Expertise in Algorithmic Prediction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.00793/</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/2402.00793/feed.xml" rel="self" type="application/rss+xml" /><item><title>Human Expertise in Algorithmic Prediction — mentioned by skumar17</title><link>https://news.ycombinator.com/item?id=42597935</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.00793:42597935</guid><pubDate>Sat, 04 Jan 2025 21:48:48 GMT</pubDate><description>&lt;p&gt;Hi there! Last month at NeurIPS (an ML conference), I read an interesting paper &amp;quot;Human Expertise in Algorithmic Prediction&amp;quot; that describes a framework for determining where ML models are outperformed by human experts. I found the authors&amp;#x27; work to be very interesting. Below, I explore their framework further and extend it to multiclass classification. My results are pretty surprising, showing that a group of modern model architectures have trouble with dogs and cats in CIFAR-10. GitHub Link: https://github.com/sunildkumar/model_indistinguishability Paper Link: https://arxiv.org/abs/2402.00793&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.00793/"&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>
