<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Regularization | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/regularization/</link><atom:link href="https://ahren09.github.io/tags/regularization/index.xml" rel="self" type="application/rss+xml"/><description>Regularization</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sat, 30 May 2026 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>Regularization</title><link>https://ahren09.github.io/tags/regularization/</link></image><item><title>TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization</title><link>https://ahren09.github.io/publication/neurips26_textreg/</link><pubDate>Sat, 30 May 2026 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/neurips26_textreg/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Prompt optimization methods often overfit to narrow training distributions, producing prompts that fail to transfer. TextReg introduces a regularized text-space optimization objective that explicitly mitigates prompt distributional overfitting.&lt;/p>
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