Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Best of Both Worlds Model Selection
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
- Model Selection in Contextual Stochastic Bandit Problems (NeurIPS 2020 · similarity 0.93)
- Improved Best-of-Both-Worlds Regret for Bandits with Delayed Feedback (NeurIPS 2025 · similarity 0.91)
- Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints (NeurIPS 2024 · similarity 0.90)
- Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting (NeurIPS 2024 · similarity 0.90)
- Sparsity-Agnostic Linear Bandits with Adaptive Adversaries (NeurIPS 2024 · similarity 0.89)
- Stochastic Bandits Robust to Adversarial Attacks (ICLR 2025 · similarity 0.89)
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.