<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bandits |</title><link>https://nick-jhlee.github.io/tags/bandits/</link><atom:link href="https://nick-jhlee.github.io/tags/bandits/index.xml" rel="self" type="application/rss+xml"/><description>Bandits</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 22 May 2026 00:00:00 +0000</lastBuildDate><image><url>https://nick-jhlee.github.io/media/icon_hu_eee4a95885829ab2.png</url><title>Bandits</title><link>https://nick-jhlee.github.io/tags/bandits/</link></image><item><title>Cumulative Distribution Regret Minimization with Max- Quantile Threshold in Multi-Armed Bandit</title><link>https://nick-jhlee.github.io/publications/kcc26/</link><pubDate>Fri, 22 May 2026 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/kcc26/</guid><description/></item><item><title>Looking Through the Mirror: Minimax-Optimal Regularized Regrets in Online Learning and Bandits</title><link>https://nick-jhlee.github.io/publications/arxiv26-regularized-regret/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/arxiv26-regularized-regret/</guid><description/></item><item><title>GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression</title><link>https://nick-jhlee.github.io/publications/aistats26-gl-lowpopart/</link><pubDate>Sat, 02 May 2026 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/aistats26-gl-lowpopart/</guid><description/></item><item><title>Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences</title><link>https://nick-jhlee.github.io/publications/arxiv26-bilinear-nash/</link><pubDate>Sun, 22 Feb 2026 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/arxiv26-bilinear-nash/</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>A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits</title><link>https://nick-jhlee.github.io/publications/neurips24/</link><pubDate>Wed, 19 Jun 2024 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/neurips24/</guid><description/></item><item><title>Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion</title><link>https://nick-jhlee.github.io/publications/aistats24/</link><pubDate>Sat, 20 Jan 2024 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/aistats24/</guid><description/></item><item><title>Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks</title><link>https://nick-jhlee.github.io/publications/opodis23/</link><pubDate>Mon, 30 Oct 2023 00:00:00 +0000</pubDate><guid>https://nick-jhlee.github.io/publications/opodis23/</guid><description/></item></channel></rss>