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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On statistical learning via the lens of compression · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1610.03592/</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/1610.03592/feed.xml" rel="self" type="application/rss+xml" /><item><title>On statistical learning via the lens of compression — mentioned by nyrikki</title><link>https://news.ycombinator.com/item?id=39865465</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.03592:39865465</guid><pubDate>Fri, 29 Mar 2024 15:45:58 GMT</pubDate><description>&lt;p&gt;PAC learning is compression. PAC learnable, Finite VC dimensionality, and the following form of compression are fully equivalent. https://arxiv.org/abs/1610.03592 Basically each individual neuron/perceptron just splits a space into two subspaces.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.03592/"&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>
