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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1809.04559/</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/1809.04559/feed.xml" rel="self" type="application/rss+xml" /><item><title>Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms — mentioned by claytonjy</title><link>https://news.ycombinator.com/item?id=36851688</link><guid isPermaLink="false">urn:hackerpapers:mention:1809.04559:36851688</guid><pubDate>Mon, 24 Jul 2023 17:46:55 GMT</pubDate><description>&lt;p&gt;You&amp;#x27;re certainly right that AI/ML is often used interchangeably, though I think the distinction is pretty clear among practitioners: AI is the big circle, ML is a subset of AI, deep learning is a subset of ML. Symbolic AI is also a subset of AI, though I&amp;#x27;m not familiar enough with it to say how much it interects the others. So ML is AI, but just a small part of it. As for papers, here&amp;#x27;s one showing GPU speedups for the 3 big GBM packages: https://arxiv.org/pdf/1809.04559.pdf. Their setup shows a 3-7x speedup for XGBoost.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1809.04559/"&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>
