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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2202.10451/</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/2202.10451/feed.xml" rel="self" type="application/rss+xml" /><item><title>SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions — mentioned by timhigins</title><link>https://news.ycombinator.com/item?id=37704267</link><guid isPermaLink="false">urn:hackerpapers:mention:2202.10451:37704267</guid><pubDate>Fri, 29 Sep 2023 13:54:07 GMT</pubDate><description>&lt;p&gt;Any reason for using the term &amp;quot;generative&amp;quot;, which may confuse readers and imply generative AI/LLMs? It&amp;#x27;s a traditional tabular autoML system, though it does learn pipelines from a corpus of Kaggle solutions, and generates pipelines with a &amp;quot;three-stage program synthesis approach&amp;quot; [1]. https://arxiv.org/pdf/2202.10451.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2202.10451/"&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>
