Research map / Bandits and online learning / Contextual bandits and Thompson sampling
A Bayesian Approach for Subset Selection in Contextual Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BRE's posterior resampling and context-aware action selection instead of LinGapE | Linear bandit best-arm identification · Synthetic data 1 and 2; Yahoo linear data | number of samples | Helped |
| Use posterior resampling and context-aware selection rather than BayesGap | Linear bandit best-arm identification · Synthetic data 1 | number of samples | Helped |
| Use a frequentist stopping rule instead of the BRE Monte Carlo stopping rule | BayesGap · Synthetic data 2 | number of samples | Hurt |
| Use BRE for Explore-K subset selection | Explore-K contextual bandits · Yahoo Webscope Dataset R6A simulated Explore-K | number of samples | Helped |
Closest papers by meaning
- Contextual Bandits for Unbounded Context Distributions (ICML 2025 · similarity 0.88)
- Active Context Selection Improves Simple Regret in Contextual Bandits (NeurIPS 2026 · similarity 0.88)
- Catoni Contextual Bandits are Robust to Heavy-tailed Rewards (ICML 2025 · similarity 0.88)
- On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice (NeurIPS 2024 · similarity 0.88)
- Generalized Linear Bandits with Limited Adaptivity (NeurIPS 2024 · similarity 0.88)
- Contextual Multinomial Logit Bandits with General Value Functions (NeurIPS 2024 · similarity 0.88)
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