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Design of Experiments for Stochastic Contextual Linear Bandits
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In the stochastic linear contextual bandit setting there exist several minimax procedures for exploration with policies that are reactive to the data being acquired. In practice, there can be a significant engineering overhead to deploy these algorithms, especially when the dataset is collected in a distributed fashion or when a human in the loop is needed to implement a different policy. Exploring with a single non-reactive policy is beneficial in such cases. Assuming some batch contexts are available, we design a single stochastic policy to collect a good dataset from which a near-optimal policy can be extracted. We present a theoretical analysis as well as numerical experiments on both synthetic and real-world datasets.
Forward citations
Cited by 3 Pith papers
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Logging Policy Design for Off-Policy Evaluation
Derives optimal logging policies for off-policy evaluation by balancing reward concentration against action coverage in known, unknown, and partially known regimes of target policy and rewards.
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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.
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