Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits

arXiv:2609.28718 · stat.ML, cs.LG, math.ST, stat.TH · Submitted 2026-09-23 · Read on arXiv

stat.ML, cs.LG, math.ST, stat.TH

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Regret — 52× in this paper · explained in 3 episodes
• Trace — 12× in this paper · explained in 2 episodes
• Contextual Bandits — 5× in this paper · explained in 3 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Contextual Bandit Problem — 1× in this paper · explained in 2 episodes
• Information Gain — 1× in this paper · explained in 2 episodes

Sources

• Linear Bandits in High Dimension and Recommendation Systems
• Diffusion Approximations for Thompson Sampling in the Small Gap Regime
• Statistical Inference under Adaptive Sampling with LinUCB
• A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
• Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
• Weak Signal Asymptotics for Sequentially Randomized Experiments
• A Tutorial on Thompson Sampling

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