3 citations · 8 across the 6 of their papers we have counts for
6 papers
Projection-Free Algorithms in Statistical Estimation
Yan Li, Chao Qu, Huan Xu
Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gra…
Communication-Efficient Projection-Free Algorithm for Distributed Optimization
Yan Li, Chao Qu, Huan Xu
Distributed optimization has gained a surge of interest in recent years. In this paper we propose a distributed projection free algorithm named Distributed Conditional Gradient Sli…
Fast Global Convergence via Landscape of Empirical Loss
Chao Qu, Yan Li, Huan Xu
While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing intere…
Non-convex Conditional Gradient Sliding
Chao Qu, Yan Li, Huan Xu
We investigate a projection free method, namely conditional gradient sliding on batched, stochastic and finite-sum non-convex problem. CGS is a smart combination of Nesterov's acce…
SAGA and Restricted Strong Convexity
Chao Qu, Yan Li, Huan Xu
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications. In this paper, we analyze SAGA on a class of n…
Linear Convergence of SVRG in Statistical Estimation
Chao Qu, Yan Li, Huan Xu
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems. It is well known that SVRG converges linearly when the objective…