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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AutoCompete: A Framework for Machine Learning Competition · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1507.02188/</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/1507.02188/feed.xml" rel="self" type="application/rss+xml" /><item><title>AutoCompete: A Framework for Machine Learning Competition — mentioned by nl</title><link>https://news.ycombinator.com/item?id=12148203</link><guid isPermaLink="false">urn:hackerpapers:mention:1507.02188:12148203</guid><pubDate>Sat, 23 Jul 2016 03:26:32 GMT</pubDate><description>&lt;p&gt;A lot of the criticisms here are about inefficiencies in the approach. This is true, but the approach here does generalise well (provided you have sufficient data). It&amp;#x27;s worth noting that Abhishek is working on the &amp;quot;automatic data science&amp;quot; problem[1] and is viewing thing through that lens. AutoML is an interesting area - there have been workshops at ICML, and good progress is being made in the area. [1] https://arxiv.org/abs/1507.02188&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1507.02188/"&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>
