Research map / Bandits and online learning / Multi-armed bandits
Recovering Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GP-based 1-step UCB instead of RogueUCB-Tuned | recovering bandits · Logistic | total reward: 446.2 -> 461.7 | Helped |
| Use GP-based 1-step Thompson sampling instead of RogueUCB-Tuned | recovering bandits · Logistic | total reward: 446.2 -> 462.6 | Helped |
| Use GP-based 1-step UCB instead of UCB-Z | recovering bandits · Logistic | total reward: 242.6 -> 461.7 | Helped |
| Use GP-based 1-step Thompson sampling instead of UCB-Z | recovering bandits · Logistic | total reward: 242.6 -> 462.6 | Helped |
| Use GP-based 1-step UCB instead of RogueUCB-Tuned | recovering bandits · Gamma | total reward: 132.7 -> 145.6 | Helped |
| Use GP-based 1-step Thompson sampling instead of RogueUCB-Tuned | recovering bandits · Gamma | total reward: 132.7 -> 156.5 | Helped |
| Use GP-based 1-step UCB instead of UCB-Z | recovering bandits · Gamma | total reward: 116.8 -> 145.6 | Helped |
| Use GP-based 1-step Thompson sampling instead of UCB-Z | recovering bandits · Gamma | total reward: 116.8 -> 156.5 | Helped |
| Increase optimistic-planning policy-evaluation budget | dRGP-TS · lookahead planning (K=10, d=4) | total reward | Helped |
Closest papers by meaning
- Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected (AAAI 2017 · similarity 0.92)
- Forced Exploration in Bandit Problems (AAAI 2024 · similarity 0.91)
- Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals (ICML 2024 · similarity 0.90)
- Adaptive Regret for Bandits Made Possible: Two Queries Suffice (ICLR 2024 · similarity 0.90)
- Greedy Algorithms for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure (NeurIPS 2025 · similarity 0.90)
- p-Mean Regret for Stochastic Bandits (AAAI 2025 · similarity 0.90)
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