Constructs a time-indexed set S_t retaining the true optimal policy uniformly over time with high probability, enabling early stopping with sample complexity O((log |Π| + log log(1/Δ_min))/Δ_min²) when the optimum is unique.
Best-Arm Identification in Linear Bandits
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We study the best-arm identification problem in linear bandit, where the rewards of the arms depend linearly on an unknown parameter $\theta^*$ and the objective is to return the arm with the largest reward. We characterize the complexity of the problem and introduce sample allocation strategies that pull arms to identify the best arm with a fixed confidence, while minimizing the sample budget. In particular, we show the importance of exploiting the global linear structure to improve the estimate of the reward of near-optimal arms. We analyze the proposed strategies and compare their empirical performance. Finally, as a by-product of our analysis, we point out the connection to the $G$-optimality criterion used in optimal experimental design.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Derives optimal logging policies for minimizing off-policy evaluation error under known, unknown, and partially known target policies and reward distributions.
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Anytime-valid Optimal Policy Identification
Constructs a time-indexed set S_t retaining the true optimal policy uniformly over time with high probability, enabling early stopping with sample complexity O((log |Π| + log log(1/Δ_min))/Δ_min²) when the optimum is unique.
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Logging Policy Design for Off-Policy Evaluation
Derives optimal logging policies for minimizing off-policy evaluation error under known, unknown, and partially known target policies and reward distributions.