no code implementations • NeurIPS 2021 • Zhiqiang Xu, Ping Li
We further give the first worst-case analysis that achieves a rate of convergence at $O(\frac{1}{\epsilon}\log\frac{1}{\epsilon})$.
no code implementations • NeurIPS 2020 • Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li
Adaptive gradient methods such as AdaGrad, RMSprop and Adam have been optimizers of choice for deep learning due to their fast training speed.
no code implementations • NeurIPS 2019 • Zhiqiang Xu, Ping Li
To promote the practical use of ALS for CCA, we propose truly alternating least-squares.
no code implementations • NeurIPS 2018 • Zhiqiang Xu
Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations.
no code implementations • 26 May 2016 • Zhiqiang Xu, Yiping Ke
We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem.
no code implementations • 19 Oct 2012 • Zhiqiang Xu
In particular, for $M=s^a$ with $a\in [0, 1/2]$, OMMP(M) can recover slowly-decaying $s$-sparse signal within $O(s^{1-a})$ iterations.