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A Private and Computationally-Efficient Estimator for Unbounded Gaussians.

, , , , и . COLT, том 178 из Proceedings of Machine Learning Research, стр. 544-572. PMLR, (2022)

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Anil Singhal

Vikrant Upadhyay

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Ashok Kumar Singhal

 

Другие публикации лиц с тем же именем

Private Mean Estimation of Heavy-Tailed Distributions., , и . COLT, том 125 из Proceedings of Machine Learning Research, стр. 2204-2235. PMLR, (2020)Privately Learning High-Dimensional Distributions., , , и . COLT, том 99 из Proceedings of Machine Learning Research, стр. 1853-1902. PMLR, (2019)A Bias-Variance-Privacy Trilemma for Statistical Estimation., , , , , и . CoRR, (2023)Differentially Private Algorithms for Learning Mixtures of Separated Gaussians., , , и . NeurIPS, стр. 168-180. (2019)New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma., , и . NeurIPS, (2022)Private Means and the Curious Incident of the Free Lunch., , , и . CoRR, (2024)Privately Learning Subspaces., и . NeurIPS, стр. 1312-1324. (2021)A Private and Computationally-Efficient Estimator for Unbounded Gaussians., , , , и . COLT, том 178 из Proceedings of Machine Learning Research, стр. 544-572. PMLR, (2022)Not All Learnable Distribution Classes are Privately Learnable., , , и . ALT, том 237 из Proceedings of Machine Learning Research, стр. 390-401. PMLR, (2024)Privately Learning High-Dimensional Distributions, , , и . (2018)cite arxiv:1805.00216.