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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.08391/</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/2310.08391/feed.xml" rel="self" type="application/rss+xml" /><item><title>How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=38182378</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.08391:38182378</guid><pubDate>Tue, 07 Nov 2023 20:31:16 GMT</pubDate><description>&lt;p&gt;&amp;gt; I&amp;#x27;m not sure folks who&amp;#x27;re putting out strong takes based on this have read this paper. They haven&amp;#x27;t read the other papers either. It&amp;#x27;s really striking to me to watch people retweet this and it get written up in pseudo-media like Business Insider when other meta-learning papers on the distributional hypothesis of inducing meta-learning &amp;amp; generalization, which are at least as relevant, can&amp;#x27;t even make a peep on specialized research subreddits - like, &amp;quot;Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression&amp;quot;, Raventós et al 2023…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.08391/"&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>
