Research map / Bandits and online learning / Multi-armed bandits
Threshold Bandits, With and Without Censored Feedback
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
- Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback (NeurIPS 2019 · similarity 0.89)
- Thresholding Bandit with Optimal Aggregate Regret (NeurIPS 2019 · similarity 0.88)
- A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms (NeurIPS 2021 · similarity 0.88)
- Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals (ICML 2024 · similarity 0.87)
- Testing the Feasibility of Linear Programs with Bandit Feedback (ICML 2024 · similarity 0.87)
- Feasible Action Search for Bandit Linear Programs via Thompson Sampling (ICML 2025 · similarity 0.87)
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.
