25 citations · 25 across the 5 of their papers we have counts for
5 papers · 1 filter
Computation of Strong Solutions to Stochastic Variational Inequalities
Yao Ji, Guanghui Lan, Jason Zhu
This paper studies the computation of strong solutions of monotone variational inequalities (VIs) with Lipschitz continuous operators. Building on the idea of accumulative regulari…
Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
Yao Ji, Guanghui Lan
Achieving optimal rates for stochastic composite convex optimization without prior knowledge of problem parameters remains a central challenge. In the deterministic setting, the au…
Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan +1
Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applica…
Can SGD Handle Heavy-Tailed Noise?
Ilyas Fatkhullin, Florian Hübler, Guanghui Lan
Stochastic Gradient Descent (SGD) is a cornerstone of large-scale optimization, yet its theoretical behavior under heavy-tailed noise -- common in modern machine learning and reinf…
Randomized First-Order Methods for Saddle Point Optimization
Cong Dang, Guanghui Lan
In this paper, we present novel randomized algorithms for solving saddle point problems whose dual feasible region is given by the direct product of many convex sets. Our algorithm…