163 citations · 1.2k across the 128 of their papers we have counts for
15 papers · 1 filter
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Zhuoning Yuan, Yan Yan, Rong Jin +1
Stagewise training strategy is widely used for learning neural networks, which runs a stochastic algorithm (e.g., SGD) starting with a relatively large step size (aka learning rate…
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…
Frank-Wolfe Method is Automatically Adaptive to Error Bound Condition
Yi Xu, Tianbao Yang
Error bound condition has recently gained revived interest in optimization. It has been leveraged to derive faster convergence for many popular algorithms, including subgradient me…
Learning Discriminators as Energy Networks in Adversarial Learning
Pingbo Pan, Yan Yan, Tianbao Yang +1
We propose a novel framework for structured prediction via adversarial learning. Existing adversarial learning methods involve two separate networks, i.e., the structured predictio…
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…