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Privacy Amplification via Random Check-Ins.

, , , , и . NeurIPS, (2020)

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Другие публикации лиц с тем же именем

Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression., , и . COLT, том 23 из JMLR Proceedings, стр. 25.1-25.40. JMLR.org, (2012)Differentially Private Matrix Completion, Revisited., , и . CoRR, (2017)Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry., , и . CoRR, (2014)Faster Differentially Private Convex Optimization via Second-Order Methods., , , и . CoRR, (2023)Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation., , , , , и . CoRR, (2024)Differentially Private Stream Processing at Scale., , , , , , , и . CoRR, (2023)Toward Training at ImageNet Scale with Differential Privacy., , , , , и . CoRR, (2022)Differentially Private Image Classification from Features., , , , и . CoRR, (2022)Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity., , , , , и . SODA, стр. 2468-2479. SIAM, (2019)Practical and Private (Deep) Learning Without Sampling or Shuffling., , , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 5213-5225. PMLR, (2021)