241 citations · 815 across the 44 of their papers we have counts for
4 papers · 2 filters
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin +2
Difference of convex (DC) functions cover a broad family of non-convex and possibly non-smooth and non-differentiable functions, and have wide applications in machine learning and…
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
Mingrui Liu, Hassan Rafique, Qihang Lin +1
In this paper, we consider first-order convergence theory and algorithms for solving a class of non-convex non-concave min-max saddle-point problems, whose objective function is we…
Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning
Hassan Rafique, Mingrui Liu, Qihang Lin +1
Min-max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex-concave min-ma…
Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization
Xi Chen, Qihang Lin, Zizhuo Wang
Stochastic optimization finds a wide range of applications in operations research and management science. However, existing stochastic optimization techniques usually require the i…