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Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation., , , , , и . ICML, OpenReview.net, (2024)Private Stochastic Convex Optimization with Optimal Rates., , , и . NeurIPS, стр. 11279-11288. (2019)Privacy Amplification for Matrix Mechanisms., , , и . ICLR, OpenReview.net, (2024)Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams., , , , и . NeurIPS, (2022)Differentially Private Model Personalization., , , , и . NeurIPS, стр. 29723-29735. (2021)Why Is Public Pretraining Necessary for Private Model Training?, , , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 10611-10627. PMLR, (2023)Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements., , , и . ICML, OpenReview.net, (2024)(Near) Dimension Independent Risk Bounds for Differentially Private Learning., и . ICML, том 32 из JMLR Workshop and Conference Proceedings, стр. 476-484. JMLR.org, (2014)Privacy-preserving Data Mining in Industry., , и . WSDM, стр. 840-841. ACM, (2019)Multi-Task Differential Privacy Under Distribution Skew., , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 17784-17807. PMLR, (2023)