Research map / Bandits and online learning / Multi-armed bandits
Minimal Exploration in Structured Stochastic 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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use OSSB's estimated minimal-exploration allocation with exploitation, estimation, and exploration phases | Structured stochastic bandits · Generic structured bandits | asymptotic regret | Helped |
| Use OSSB instead of the compared algorithms | Linear bandits · Linear bandit setting with 81 arms and d=3 | average regret | Helped |
Closest papers by meaning
- Structure Adaptive Algorithms for Stochastic Bandits (ICML 2020 · similarity 0.93)
- A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms (NeurIPS 2021 · similarity 0.92)
- Greedy Algorithms for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure (NeurIPS 2025 · similarity 0.91)
- Bayesian Regret Minimization in Offline Bandits (ICML 2024 · similarity 0.91)
- Fusing Reward and Dueling Feedback in Stochastic Bandits (ICML 2025 · similarity 0.90)
- Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates (NeurIPS 2024 · 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.