Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Optimal Regret for Policy Optimization in Contextual Bandits
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
- An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits (NeurIPS 2020 · similarity 0.92)
- Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback (NeurIPS 2025 · similarity 0.92)
- Bayesian Regret Minimization in Offline Bandits (ICML 2024 · similarity 0.91)
- On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice (NeurIPS 2024 · similarity 0.90)
- Active Context Selection Improves Simple Regret in Contextual Bandits (NeurIPS 2026 · similarity 0.90)
- Second Order Bounds for Contextual Bandits with Function Approximation (ICLR 2025 · similarity 0.90)
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.
