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Being Robust (in High Dimensions) Can Be Practical.

, , , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 999-1008. PMLR, (2017)

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A Primer on Private Statistics, and . (2020)Which Distribution Distances are Sublinearly Testable?, , and . (2017)cite arxiv:1708.00002Comment: To appear in SODA 2018.Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians., and . CoRR, (2013)INSPECTRE: Privately Estimating the Unseen., , , and . J. Priv. Confidentiality, (2020)Private Identity Testing for High-Dimensional Distributions., , , , and . CoRR, (2019)Sever: A Robust Meta-Algorithm for Stochastic Optimization., , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 1596-1606. PMLR, (2019)Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data., , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 10633-10660. PMLR, (2022)Not All Learnable Distribution Classes are Privately Learnable., , , and . ALT, volume 237 of Proceedings of Machine Learning Research, page 390-401. PMLR, (2024)Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization., , and . NeurIPS, page 26409-26421. (2021)CoinPress: Practical Private Mean and Covariance Estimation., , , and . NeurIPS, (2020)