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A Combinatorial, Primal-Dual Approach to Semidefinite Programs.

, и . J. ACM, 63 (2): 12:1-12:35 (2016)

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Devika Kale

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Preface: Conference on Learning Theory (COLT), 2017., и . COLT, том 65 из Proceedings of Machine Learning Research, стр. 1-3. PMLR, (2017)Stochastic Negative Mining for Learning with Large Output Spaces., , , , , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 1940-1949. PMLR, (2019)Estimating Training Data Influence by Tracking Gradient Descent., , , и . CoRR, (2020)Efficient aggregation algorithms for probabilistic data., , и . SODA, стр. 346-355. SIAM, (2007)Agnostic Learnability of Halfspaces via Logistic Loss., , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 10068-10103. PMLR, (2022)Estimating Training Data Influence by Tracing Gradient Descent., , , и . NeurIPS, (2020)Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria., и . NIPS, стр. 625-632. Curran Associates, Inc., (2007)Parameter-Free Online Learning via Model Selection., , , и . NIPS, стр. 6020-6030. (2017)Private Matrix Approximation and Geometry of Unitary Orbits., , , , и . COLT, том 178 из Proceedings of Machine Learning Research, стр. 3547-3588. PMLR, (2022)Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements., , , и . ICML, OpenReview.net, (2024)