8 citations · 15 across the 12 of their papers we have counts for
6 papers · 1 filter
Adaptive Delayed-Update Cyclic Algorithm for Variational Inequalities
Yi Wei, Xufeng Cai, Jelena Diakonikolas
Cyclic block coordinate methods are a fundamental class of first-order algorithms, widely used in practice for their simplicity and strong empirical performance. Yet, their theoret…
Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm
Xufeng Cai, Jason M. Altschuler, Jelena Diakonikolas
In 1960, Osborne proposed a simple iterative algorithm for matrix balancing with outstanding numerical performance. Today, it is the default preconditioning procedure before eigenv…
Last Iterate Convergence of Incremental Methods and Applications in Continual Learning
Xufeng Cai, Jelena Diakonikolas
Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Ye…
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…
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…
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
Xufeng Cai, Chaobing Song, Cristóbal Guzmán +1
We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel varian…