<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NeurIPS2026 | HERCOLE Lab</title><link>https://hercolelab.netlify.app/tags/neurips2026/</link><atom:link href="https://hercolelab.netlify.app/tags/neurips2026/index.xml" rel="self" type="application/rss+xml"/><description>NeurIPS2026</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 25 Sep 2026 00:00:00 +0000</lastBuildDate><image><url>https://hercolelab.netlify.app/media/icon_hu12080033123456965919.png</url><title>NeurIPS2026</title><link>https://hercolelab.netlify.app/tags/neurips2026/</link></image><item><title>TRACER accepted at NeurIPS 2026</title><link>https://hercolelab.netlify.app/news/26neurips-tracer/</link><pubDate>Fri, 25 Sep 2026 00:00:00 +0000</pubDate><guid>https://hercolelab.netlify.app/news/26neurips-tracer/</guid><description>&lt;p>We are pleased to announce that &lt;strong>&amp;ldquo;TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation&amp;rdquo;&lt;/strong> has been accepted at &lt;strong>NeurIPS 2026&lt;/strong>. The paper is by Ziheng Chen, Jiali Cheng, Zezhong Fan, Hadi Amiri, Diyuan Wu, Gabriele Tolomei, and Yang Zhang.&lt;/p>
&lt;p>Generative recommender systems encode items as sequences of semantic IDs. Because different items can share parts of these sequences, removing a sensitive or unwanted concept can also harm recommendations that should be retained. &lt;strong>TRACER&lt;/strong> addresses this problem by reassigning concept-related items to alternative tokens and using a regularizer to preserve consistency among retained items.&lt;/p>
&lt;p>Experiments on real-world recommendation datasets show effective concept removal with better preservation of recommendation quality than existing unlearning baselines. Read the &lt;a href="https://arxiv.org/abs/2606.07688" target="_blank" rel="noopener">paper on arXiv&lt;/a>.&lt;/p></description></item></channel></rss>