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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Understanding Short-Horizon Bias in Stochastic Meta-Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1803.02021/</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/1803.02021/feed.xml" rel="self" type="application/rss+xml" /><item><title>Understanding Short-Horizon Bias in Stochastic Meta-Optimization — mentioned by Straw</title><link>https://news.ycombinator.com/item?id=21145219</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.02021:21145219</guid><pubDate>Thu, 03 Oct 2019 09:50:34 GMT</pubDate><description>&lt;p&gt;Unfortunately, the 1-step optimal learning rate often differs massively from the long-horizon best choice: https://arxiv.org/abs/1803.02021 Due to the fact that larger LRs can result in worse immediate performance but better progress in low signal-to-noise ratio directions.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.02021/"&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>
