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On the accuracy and efficiency of group-wise clipping in differentially private optimization.

, , , , and . CoRR, (2023)

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Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach., , , and . CoRR, (2023)Coupling public and private gradient provably helps optimization., , , , and . CoRR, (2023)MISNN: Multiple Imputation via Semi-parametric Neural Networks., , , and . PAKDD (1), volume 13935 of Lecture Notes in Computer Science, page 430-442. Springer, (2023)Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing., , , and . NeurIPS, page 9361-9371. (2019)Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data., , and . ACML, volume 189 of Proceedings of Machine Learning Research, page 265-279. PMLR, (2022)Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability., , , and . ECML/PKDD (4), volume 13716 of Lecture Notes in Computer Science, page 604-619. Springer, (2022)Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity., , , and . ECML/PKDD (3), volume 14171 of Lecture Notes in Computer Science, page 343-359. Springer, (2023)On the accuracy and efficiency of group-wise clipping in differentially private optimization., , , , and . CoRR, (2023)Differentially Private Optimizers Can Learn Adversarially Robust Models., and . Trans. Mach. Learn. Res., (2023)On the Convergence and Calibration of Deep Learning with Differential Privacy., , , and . Trans. Mach. Learn. Res., (2023)