52 citations · 102 across the 8 of their papers we have counts for
6 papers · 1 filter
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan, Yi Xu, Qihang Lin +2
Epoch gradient descent method (a.k.a. Epoch-GD) proposed by Hazan and Kale (2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal…
On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Optimization
Yi Xu, Zhuoning Yuan, Sen Yang +2
Extrapolation is a well-known technique for solving convex optimization and variational inequalities and recently attracts some attention for non-convex optimization. Several recen…
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Yi Xu, Rong Jin, Tianbao Yang
Stochastic Proximal Gradient (SPG) methods have been widely used for solving optimization problems with a simple (possibly non-smooth) regularizer in machine learning and statistic…
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…
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen, Yi Xu, Haoyuan Hu +1
In this paper, we propose a new SVRG-style acceleated stochastic algorithm for solving a family of non-convex optimization problems whose objective consists of a sum of smooth…