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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Algorithm Runtime Prediction: Methods &amp; Evaluation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1211.0906/</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/1211.0906/feed.xml" rel="self" type="application/rss+xml" /><item><title>Algorithm Runtime Prediction: Methods &amp; Evaluation — mentioned by Mathnerd314</title><link>https://news.ycombinator.com/item?id=13232197</link><guid isPermaLink="false">urn:hackerpapers:mention:1211.0906:13232197</guid><pubDate>Wed, 21 Dec 2016 20:09:29 GMT</pubDate><description>&lt;p&gt;&amp;gt; You could run a little benchmark and adjust the timing based on that, but there are too many factors. Or you could train an ML model, like so many academics have been doing: https://arxiv.org/pdf/1211.0906.pdf http://prl.korea.ac.kr/~pronto/home/papers/sas14-leohyi.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1211.0906/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
