Research map / Bandits and online learning / Multi-armed bandits
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling (NeurIPS 2026 · similarity 0.95)
- Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox (NeurIPS 2024 · similarity 0.89)
- Variance-Aware Feel-Good Thompson Sampling for Contextual Bandits (NeurIPS 2025 · similarity 0.88)
- On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization (NeurIPS 2026 · similarity 0.88)
- Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown (NeurIPS 2025 · similarity 0.88)
- Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret (NeurIPS 2026 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
