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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2203.02433/</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/2203.02433/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights — mentioned by bglazer</title><link>https://news.ycombinator.com/item?id=30632305</link><guid isPermaLink="false">urn:hackerpapers:mention:2203.02433:30632305</guid><pubDate>Thu, 10 Mar 2022 20:55:58 GMT</pubDate><description>&lt;p&gt;&amp;gt; I&amp;#x27;m definitely awaiting for traveling salesman solver or similar, guided by NN, solving things faster and reaching optimality more frequently that optimized heuristic algos. There was a competition for exactly this at Neurips 2021 https://www.ecole.ai/2021/ml4co-competition/ Not sure how much they improved over handcrafted heuristics, but the summary paper may give some insights https://arxiv.org/abs/2203.02433&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2203.02433/"&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>
