61 citations · 750 across the 66 of their papers we have counts for
12 papers · 1 filter
MR-GAN: Manifold Regularized Generative Adversarial Networks
Qunwei Li, Bhavya Kailkhura, Rushil Anirudh +3
Despite the growing interest in generative adversarial networks (GANs), training GANs remains a challenging problem, both from a theoretical and a practical standpoint. To address…
Minimax Estimation of Neural Net Distance
Kaiyi Ji, Yingbin Liang
An important class of distance metrics proposed for training generative adversarial networks (GANs) is the integral probability metric (IPM), in which the neural net distance captu…
SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms
Zhe Wang, Kaiyi Ji, Yi Zhou +2
SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonc…
Cubic Regularization with Momentum for Nonconvex Optimization
Zhe Wang, Yi Zhou, Yingbin Liang +1
Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. Howev…
A Note on Inexact Condition for Cubic Regularized Newton's Method
Zhe Wang, Yi Zhou, Yingbin Liang +1
This note considers the inexact cubic-regularized Newton's method (CR), which has been shown in \cite{Cartis2011a} to achieve the same order-level convergence rate to a secondary s…
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
Yi Zhou, Zhe Wang, Yingbin Liang
Cubic-regularized Newton's method (CR) is a popular algorithm that guarantees to produce a second-order stationary solution for solving nonconvex optimization problems. However, ex…