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Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis.

D. Garber, O. Shamir, и N. Srebro. ICML, том 70 из Proceedings of Machine Learning Research, стр. 1203-1212. PMLR, (2017)

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Andrä Gärber

Yvo Gärber

Thomas Garber

Rolf Garber

Jörn Garber

Другие публикации лиц с тем же именем

Linear convergence of Frank-Wolfe for rank-one matrix recovery without strong convexity.D. Garber. Math. Program., 199 (1): 87-121 (мая 2023)From Oja's Algorithm to the Multiplicative Weights Update Method with Applications.D. Garber. CoRR, (2023)Online Principal Components Analysis.C. Boutsidis, D. Garber, Z. Karnin, и E. Liberty. SODA, стр. 887-901. SIAM, (2015)On the Convergence of Projected-Gradient Methods with Low-Rank Projections for Smooth Convex Minimization over Trace-Norm Balls and Related Problems.D. Garber. SIAM J. Optim., 31 (1): 727-753 (2021)Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator.L. Danon, и D. Garber. NeurIPS, (2022)Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity.D. Garber. NeurIPS, (2020)Frank-Wolfe with a Nearest Extreme Point Oracle.D. Garber, и N. Wolf. COLT, том 134 из Proceedings of Machine Learning Research, стр. 2103-2132. PMLR, (2021)New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees.D. Garber, и B. Kretzu. COLT, том 178 из Proceedings of Machine Learning Research, стр. 2326-2359. PMLR, (2022)Linear Convergence of Frank-Wolfe for Rank-One Matrix Recovery Without Strong Convexity.D. Garber. CoRR, (2019)Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes.D. Garber, и O. Meshi. NIPS, стр. 1001-1009. (2016)

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