8 citations · 15 across the 12 of their papers we have counts for
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math.OC2023★ 1 cited
Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds
Xufeng Cai, Cheuk Yin Lin, Jelena Diakonikolas
Stochastic gradient descent (SGD) is perhaps the most prevalent optimization method in modern machine learning. Contrary to the empirical practice of sampling from the datasets wit…
math.OC2023★ 1 cited
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
Xufeng Cai, Chaobing Song, Stephen J. Wright +1
Nonconvex optimization is central in solving many machine learning problems, in which block-wise structure is commonly encountered. In this work, we propose cyclic block coordinate…