Research map / Bandits and online learning / Multi-armed bandits
Verification Based Solution for Structured MAB Problems
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 |
|---|---|---|---|
| Explore-then-verify framework with δ-independent exploration followed by verification at confidence δ/(2r²) | Best arm identification in structured MAB problems · High-confidence regime (small δ) | expected query complexity | Helped |
| Set ε = min{1/3, 1/log(1/δ)} in the framework | Explore-verify framework · General structured MAB | expected query complexity | Helped |
| Unimodal line-graph: replace ∆Γx with ∆x in the δ-independent term | Unimodal bandits vs OSUB · Line graph unimodal bandits | query complexity constant term | Helped |
Closest papers by meaning
- The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits (NeurIPS 2026 · similarity 0.90)
- Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback (NeurIPS 2026 · similarity 0.89)
- Near Optimal Best Arm Identification for Clustered Bandits (ICML 2025 · similarity 0.89)
- Protocols for Verifying Smooth Strategies in Bandits and Games (NeurIPS 2025 · similarity 0.89)
- On the Asymptotic Optimality of Confidence Interval Based Algorithms for Fixed Confidence MABs (AAAI 2025 · similarity 0.89)
- Optimal Multi-Fidelity Best-Arm Identification (NeurIPS 2024 · 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.