A Unified Optimism-Agnostic Framework for Linear Bandits over Spherical Action Sets

arXiv:2609.32149 · stat.ML, cs.LG · Submitted 2026-09-26 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Regret — 73× in this paper · explained in 3 episodes
• Thompson Sampling — 20× in this paper
• Upper Confidence Bound — 4× in this paper
• Contextual Bandits — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Minimax Regret — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
• Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds

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