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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)(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)Privacy Amplification via Random Check-Ins., , , , and . NeurIPS, (2020)Practical and Private (Deep) Learning Without Sampling or Shuffling., , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 5213-5225. PMLR, (2021)Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression., , and . COLT, volume 23 of JMLR Proceedings, page 25.1-25.40. JMLR.org, (2012)Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds., , and . FOCS, page 464-473. IEEE Computer Society, (2014)To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout, , , and . (2015)cite arxiv:1503.02031Comment: Currently under review for ICML 2015.Differentially Private Online Learning., , and . COLT, volume 23 of JMLR Proceedings, page 24.1-24.34. JMLR.org, (2012)Differentially Private Learning with Kernels., and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 118-126. JMLR.org, (2013)