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Faster Differentially Private Convex Optimization via Second-Order Methods., , , and . CoRR, (2023)Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity., , , , , and . SODA, page 2468-2479. SIAM, (2019)Practical and Private (Deep) Learning Without Sampling or Shuffling., , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 5213-5225. PMLR, (2021)Privacy Amplification via Random Check-Ins., , , , and . NeurIPS, (2020)(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping., , and . COLT, volume 178 of Proceedings of Machine Learning Research, page 1126-1166. PMLR, (2022)Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds., , and . FOCS, page 464-473. IEEE Computer Society, (2014)Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy, , and . CoRR, (2012)Differentially Private Matrix Completion Revisited., , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 2220-2229. PMLR, (2018)(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits., and . UAI, page 592-601. AUAI Press, (2015)Privacy Amplification for Matrix Mechanisms., , , and . CoRR, (2023)