Algorithms and matching lower bounds for s-sparse contextual bandits yield Õ((s/ε² + |A|/ε) log |Π|/δ) samples to output an ε-optimal policy.
Decision making in changing environments: Robustness, query-based learning, and differential privacy,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Derives explicit minimax quantile lower bounds for Gaussian mean estimation and K-armed bandits under interactive decision making and MI privacy, with log(1/δ)/n and √(KT log(1/δ)) scalings.
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The Sample Complexity of Multiclass and Sparse Contextual Bandits
Algorithms and matching lower bounds for s-sparse contextual bandits yield Õ((s/ε² + |A|/ε) log |Π|/δ) samples to output an ε-optimal policy.
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Minimax Quantile Lower Bounds for Interactive Statistical Decision Making with Privacy
Derives explicit minimax quantile lower bounds for Gaussian mean estimation and K-armed bandits under interactive decision making and MI privacy, with log(1/δ)/n and √(KT log(1/δ)) scalings.