<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sanghwa Kim |</title><link>https://nick-jhlee.github.io/authors/sanghwa-kim/</link><atom:link href="https://nick-jhlee.github.io/authors/sanghwa-kim/index.xml" rel="self" type="application/rss+xml"/><description>Sanghwa Kim</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 25 May 2026 00:00:00 +0000</lastBuildDate><item><title>Instance-Optimal Estimation with Multiple LLM Judges on a Budget</title><link>https://nick-jhlee.github.io/publications/arxiv26-adaptive-llm-judge/</link><pubDate>Mon, 25 May 2026 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/arxiv26-adaptive-llm-judge/</guid><description/></item><item><title>A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions</title><link>https://nick-jhlee.github.io/publications/arxiv26-corrupted-glm/</link><pubDate>Thu, 12 Feb 2026 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/arxiv26-corrupted-glm/</guid><description/></item><item><title>Preliminary Empirical Study of Low-Rank, Hierarchical Gaussian Linear Bandits</title><link>https://nick-jhlee.github.io/publications/ksc25/</link><pubDate>Tue, 16 Dec 2025 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/ksc25/</guid><description/></item></channel></rss>