Research map / Bandits and online learning / Multi-armed bandits
Combinatorial Pure Exploration of Causal 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 |
|---|---|---|---|
| add gap-dependent observation threshold (observational data reuse with LUCB adaptivity) in CCPE-BGLM | CCPE-BGLM on binary generalized linear models · theoretical sample complexity | sample complexity | Helped |
| interleave observational estimation step with interventional LUCB steps (CCPE-General) | CCPE-General on general graphs with hidden variables · theoretical sample complexity | sample complexity | Helped |
Closest papers by meaning
- Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits (NeurIPS 2024 · similarity 0.91)
- Linear Causal Bandits: Unknown Graph and Soft Interventions (NeurIPS 2024 · similarity 0.90)
- Adaptive Online Experimental Design for Causal Discovery (ICML 2024 · similarity 0.89)
- Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals (ICML 2024 · similarity 0.89)
- Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach (AAAI 2024 · similarity 0.88)
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · 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.