29 citations · 49 across the 6 of their papers we have counts for
6 papers
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu +6
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…
On Computation and Generalization of GANs with Spectrum Control
Haoming Jiang, Zhehui Chen, Minshuo Chen +3
Generative Adversarial Networks (GANs), though powerful, is hard to train. Several recent works (brock2016neural,miyato2018spectral) suggest that controlling the spectra of weight…
Learning to Defend by Learning to Attack
Haoming Jiang, Zhehui Chen, Yuyang Shi +2
Adversarial training provides a principled approach for training robust neural networks. From an optimization perspective, adversarial training is essentially solving a bilevel opt…
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
Zhehui Chen, Xingguo Li, Lin F. Yang +2
We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a mini…
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
Tianyi Liu, Zhehui Chen, Enlu Zhou +1
Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning, e.g., training deep neural networks, vari…
Dropping Convexity for More Efficient and Scalable Online Multiview Learning
Zhehui Chen, Lin F. Yang, Chris J. Li +1
Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to…