Under LDP, optimal online stopping uses binary reports and achieves competitive ratio e^ε/(n-1+e^ε) vs the non-private online optimum and (1+e^{-ε})/2 vs the LDP prophet.
arXiv preprint arXiv:2007.03121 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.
Derives privacy-dependent lower bounds for fixed-confidence BAI and gives asymptotically optimal DP Top-Two algorithms for local and global models.
Replaces determinant growth with generalized Rayleigh quotient for rare switching in private linear bandits to control worst-direction volume despite non-monotonic design matrices from noise.
citing papers explorer
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Prophet Inequalities under Local Differential Privacy
Under LDP, optimal online stopping uses binary reports and achieves competitive ratio e^ε/(n-1+e^ε) vs the non-private online optimum and (1+e^{-ε})/2 vs the LDP prophet.
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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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On the Sample Complexity of Differentially Private Policy Optimization
Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.
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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.
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When Determinants Are Not Enough: Private Rare Switching
Replaces determinant growth with generalized Rayleigh quotient for rare switching in private linear bandits to control worst-direction volume despite non-monotonic design matrices from noise.