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Robust Algorithms for TSP and Steiner Tree., , and . ACM Trans. Algorithms, 19 (2): 12:1-12:37 (April 2023)Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks., , , and . CoRR, (2023)Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets., , and . COLT, volume 195 of Proceedings of Machine Learning Research, page 1730-1773. PMLR, (2023)Robust Algorithms for TSP and Steiner Tree., , and . ICALP, volume 168 of LIPIcs, page 54:1-54:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2020)Public Data-Assisted Mirror Descent for Private Model Training., , , , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 517-535. PMLR, (2022)Correlated Noise Provably Beats Independent Noise for Differentially Private Learning., , , , , and . CoRR, (2023)Online Service with Delay., , , and . ACM Trans. Algorithms, 17 (3): 23:1-23:31 (2021)Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC., and . NeurIPS, (2020)Why Is Public Pretraining Necessary for Private Model Training?, , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 10611-10627. PMLR, (2023)How Compression and Approximation Affect Efficiency in String Distance Measures., , , and . SODA, page 2867-2919. SIAM, (2022)