The paper establishes the first tilde O(epsilon^{-1}) upper bounds and matching lower bounds for forward-KL-regularized offline contextual bandits under single-policy concentrability in both tabular and general function approximation settings.
International conference on machine learning , pages=
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
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Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
The paper establishes the first tilde O(epsilon^{-1}) upper bounds and matching lower bounds for forward-KL-regularized offline contextual bandits under single-policy concentrability in both tabular and general function approximation settings.
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Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.