The minimax optimal excess-risk rate for pure ε-DP heavy-tailed SCO is characterized up to logarithmic factors, with a polynomial-time algorithm based on Lipschitz extensions of the empirical loss and a nearly matching lower bound.
High-probability minimax lower bounds
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Realisable epsilon-contamination models for MNAR data yield minimax mean estimation rates that decompose into MCAR plus robust terms and remain consistent for Gaussian bases even as missingness and epsilon both tend to 1.
OGPO is a sample-efficient off-policy method for full finetuning of generative control policies that reaches SOTA on robotic manipulation tasks and can recover from poor behavior-cloning initializations without expert data.
The authors instantiate a generalized-Fano framework using squared Hellinger distance to derive explicit Bayesian CVaR lower bounds for interactive decision problems including Gaussian bandits.
ULS provides minimax-optimal estimation of remaining-data parameters in machine unlearning with limited access and decomposes error into oracle plus unlearning cost terms.
Derives instance-specific lower bounds on sample complexity for rank-adaptive matrix estimation and proposes a least-squares plus universal singular-value-thresholding algorithm whose finite-sample error nearly matches those bounds.
citing papers explorer
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Optimal Rates for Pure $\varepsilon$-Differentially Private Stochastic Convex Optimization with Heavy Tails
The minimax optimal excess-risk rate for pure ε-DP heavy-tailed SCO is characterized up to logarithmic factors, with a polynomial-time algorithm based on Lipschitz extensions of the empirical loss and a nearly matching lower bound.
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Estimation beyond Missing (Completely) at Random
Realisable epsilon-contamination models for MNAR data yield minimax mean estimation rates that decompose into MCAR plus robust terms and remain consistent for Gaussian bases even as missingness and epsilon both tend to 1.
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OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
OGPO is a sample-efficient off-policy method for full finetuning of generative control policies that reaches SOTA on robotic manipulation tasks and can recover from poor behavior-cloning initializations without expert data.
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Instantiating Bayesian CVaR lower bounds in Interactive Decision Making Problems
The authors instantiate a generalized-Fano framework using squared Hellinger distance to derive explicit Bayesian CVaR lower bounds for interactive decision problems including Gaussian bandits.
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Efficient machine unlearning with minimax optimality
ULS provides minimax-optimal estimation of remaining-data parameters in machine unlearning with limited access and decomposes error into oracle plus unlearning cost terms.
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Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Derives instance-specific lower bounds on sample complexity for rank-adaptive matrix estimation and proposes a least-squares plus universal singular-value-thresholding algorithm whose finite-sample error nearly matches those bounds.