Research map / Bandits and online learning / Multi-armed bandits
PAC Identification of a Bandit Arm Relative to a Reward Quantile
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use F2 instead of LUCB (subset size up to m) for single-arm quantile identification | F2 · 10-armed Bernoulli instances I1-I3, m=4, eps=0 | sample complexity | Helped |
| increase memory/batch size of P2 (2, 30, 77) (ablation) | P2 · 4-group instances with n up to 3000, rho=0.1, eps=0.05 | sample complexity | Helped |
Closest papers by meaning
- PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits (ICML 2019 · similarity 0.93)
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- PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem (ICML 2016 · similarity 0.91)
- Instance-optimal PAC Algorithms for Contextual Bandits (NeurIPS 2022 · similarity 0.89)
- Optimal Multi-Fidelity Best-Arm Identification (NeurIPS 2024 · similarity 0.88)
- Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits (NeurIPS 2024 · similarity 0.88)
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