<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Shrinkage on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/shrinkage/</link><description>Recent content in Shrinkage on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Thu, 09 Jul 2026 21:02:19 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/shrinkage/index.xml" rel="self" type="application/rss+xml"/><item><title>Regularised Regression</title><link>https://abaj.ai/wiki/ml/supervised/regression/regularised/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ml/supervised/regression/regularised/</guid><description>&lt;p>this page collects the closed-form solutions to regularised regression (where they exist) and the iterative approximations we fall back on (where they don&amp;rsquo;t).&lt;span class="margin-note" data-note="ridge has a closed form. lasso does not, except in one charmed special case.">
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along the way we will see that regularisation is not an ad-hoc hack but a perfectly sensible artefact of estimation: it drops straight out of MAP (maximum a posteriori) inference once you put a prior on the coefficients.&lt;/p></description></item></channel></rss>