183 citations · 474 across the 30 of their papers we have counts for
8 papers · 1 filter
The Row Normalization Puzzle in Muon
Jiayu Zhang, Tianyi Lin
This paper examines how row-wise renormalization affects Muon, focusing on the gap between NorMuon's worst-case guarantees and its practical performance (Li et al.). Despite its gr…
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
Yaodong Yu, Tianyi Lin, Eric Mazumdar +1
Distributionally robust supervised learning (DRSL) is emerging as a key paradigm for building reliable machine learning systems for real-world applications -- reflecting the need f…
A Variational Inequality Approach to Bayesian Regression Games
Wenshuo Guo, Michael I. Jordan, Tianyi Lin
Bayesian regression games are a special class of two-player general-sum Bayesian games in which the learner is partially informed about the adversary's objective through a Bayesian…
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin, Chi Jin, Michael I. Jordan
We consider nonconvex-concave minimax problems, , where is nonconvex in but concave…
Sparsemax and Relaxed Wasserstein for Topic Sparsity
Tianyi Lin, Zhiyue Hu, Xin Guo
Topic sparsity refers to the observation that individual documents usually focus on several salient topics instead of covering a wide variety of topics, and a real topic adopts a n…
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability
Linbo Qiao, Tianyi Lin, Qi Qin +1
In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG) approach for solving a wide spectrum of regularized stochastic minimization problems, where the regulariza…
An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs
Linbo Qiao, Wei Liu, Steven Hoi
The stationary point of Problem 2 is NOT the stationary point of Problem 1. We are sorry and we are working on fixing this error.
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
Tianyi Lin, Linbo Qiao, Teng Zhang +2
We consider a wide range of regularized stochastic minimization problems with two regularization terms, one of which is composed with a linear function. This optimization model abs…