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Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry.

, , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 393-403. PMLR, (2021)

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A Complete Characterization of Statistical Query Learning with Applications to Evolvability.. Electron. Colloquium Comput. Complex., (2010)No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"., , and . CoRR, (2022)Federated Learning with Differential Privacy for End-to-End Speech Recognition., , , , , and . CoRR, (2023)Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity., , , , , and . SODA, page 2468-2479. SIAM, (2019)Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization., , and . SODA, page 1265-1277. SIAM, (2017)Interaction is necessary for distributed learning with privacy or communication constraints., and . STOC, page 450-462. ACM, (2020)Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 393-403. PMLR, (2021)Optimal Hardness Results for Maximizing Agreements with Monomials.. CCC, page 226-236. IEEE Computer Society, (2006)On The Power of Membership Queries in Agnostic Learning.. J. Mach. Learn. Res., (2009)Open Problem: The Statistical Query Complexity of Learning Sparse Halfspaces.. COLT, volume 35 of JMLR Workshop and Conference Proceedings, page 1283-1289. JMLR.org, (2014)