The first study of unlearning in offline stochastic multi-armed bandits formalizes privacy constraints and delivers adaptive algorithms with performance guarantees and lower bounds for single- and multi-source scenarios under fixed-sample and distribution models.
arXiv preprint arXiv:1905.12298 , year=
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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.
Derives privacy-dependent lower bounds for fixed-confidence BAI and gives asymptotically optimal DP Top-Two algorithms for local and global models.
citing papers explorer
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Unlearning Offline Stochastic Multi-Armed Bandits
The first study of unlearning in offline stochastic multi-armed bandits formalizes privacy constraints and delivers adaptive algorithms with performance guarantees and lower bounds for single- and multi-source scenarios under fixed-sample and distribution models.
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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.
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Differentially Private Best-Arm Identification
Derives privacy-dependent lower bounds for fixed-confidence BAI and gives asymptotically optimal DP Top-Two algorithms for local and global models.