2 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization
Cheuk Yin Lin, Chaobing Song, Jelena Diakonikolas
Exploiting partial first-order information in a cyclic way is arguably the most natural strategy to obtain scalable first-order methods. However, despite their wide use in practice…
Coordinate Linear Variance Reduction for Generalized Linear Programming
Chaobing Song, Cheuk Yin Lin, Stephen J. Wright +1
We study a class of generalized linear programs (GLP) in a large-scale setting, which includes simple, possibly nonsmooth convex regularizer and simple convex set constraints. By r…
Parameter-free Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin +1
Projection-free conditional gradient (CG) methods are the algorithms of choice for constrained optimization setups in which projections are often computationally prohibitive but li…