arXiv Statistics ML
Thompson Sampling for Learning in Zero-Sum Games
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arXiv Machine Learning
Oct 15
High-Probability Regret Bounds for Feel-Good Thompson Sampling
The paper presents the first high‑probability regret bounds for both cumulative and current‑step variants of Feel‑Good Thompson Sampling (FGTS) in contextual bandits with general realizable function approximation. The bounds rely on decoupling coefficients and prior mass near the true reward function, and in the linear setting they achieve ≠d√T log(2T)+√T log(1/δ) regret with probability at least 1‑δ. This matches the dimension and horizon dependence of upper‑confidence‑bound algorithms up to logarithmic factors, while the high‑probability cost adds only linear dependence on log(1/δ).
arXiv Machine Learning
Oct 2
Regret Analysis of Retry-Based Bandits
arXiv:2605.20854v3 Announce Type: replace Abstract: We provide the first regret analysis of ReMax in stochastic multi-armed bandits. Originally introduced for reinforcement learning, ReMax is motivat...
arXiv Machine Learning
Aug 11
Regret, equilibrium, and learning in games: A guided tour
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv Machine Learning
Jun 2
Variance-sensitive Thompson sampling for generalised linear bandits, revisited
arXiv:2606. 00431v1 Announce Type: new Abstract: We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits.
arXiv Machine Learning
19h ago
Characterizing Oblivious Common-Reward Multi-Player Bandits: Universal Stationarity, Coordination Traps, and Certified Global Rates
arXiv:2610.29673v1 Announce Type: new Abstract: This paper presents a theoretical analysis of the oblivious common-reward multi-player multi-armed bandit (OC-MPMAB), a minimal multi-player bandit mod...
arXiv Machine Learning
Jun 16
Sharp analysis of linear ensemble sampling
arXiv:2602. 08026v2 Announce Type: replace Abstract: We analyse linear ensemble sampling (ES) with standard Gaussian perturbations in stochastic linear bandits.