163 citations · 1.2k across the 128 of their papers we have counts for
9 papers · 1 filter
NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization
Yi Xu, Rong Jin, Tianbao Yang
Accelerated gradient (AG) methods are breakthroughs in convex optimization, improving the convergence rate of the gradient descent method for optimization with smooth functions. Ho…
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
Yi Xu, Rong Jin, Tianbao Yang
Two classes of methods have been proposed for escaping from saddle points with one using the second-order information carried by the Hessian and the other adding the noise into the…
Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence
Mingrui Liu, Tianbao Yang
In this paper, we study stochastic non-convex optimization with non-convex random functions. Recent studies on non-convex optimization revolve around establishing second-order conv…
On Noisy Negative Curvature Descent: Competing with Gradient Descent for Faster Non-convex Optimization
Mingrui Liu, Tianbao Yang
The Hessian-vector product has been utilized to find a second-order stationary solution with strong complexity guarantee (e.g., almost linear time complexity in the problem's dimen…
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer
Tianbao Yang, Zhe Li, Lijun Zhang
In this paper, we present a simple analysis of {\bf fast rates} with {\it high probability} of {\bf empirical minimization} for {\it stochastic composite optimization} over a finit…
SEP-Nets: Small and Effective Pattern Networks
Zhe Li, Xiaoyu Wang, Xutao Lv +1
While going deeper has been witnessed to improve the performance of convolutional neural networks (CNN), going smaller for CNN has received increasing attention recently due to its…