Publications
APIK: Active physics-informed kriging model with partial differential equations
Jialei Chen, Zhehui Chen, Chuck Zhang, C.F. Jeff Wu [arXiv]
SIAM/ASA Journal on Uncertainty Quantification 10 (1), 2022
Learning to Defense by Learning to Attack
Haoming Jiang*, Zhehui Chen*, Yuyang Shi, Bo Dai, and Tuo Zhao [arXiv]
International Conference on Artificial Intelligence and Statistics, PMLR 130:577-585, 2021Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations
Tianyi Liu, Zhehui Chen, Enlu Zhou, and Tuo Zhao [arXiv]
Informs Stochastic Systems 11 (4), 265–281, 2021On Landscape of Lagrangian Functions and Stochastic Algorithms for Constrained Nonconvex Optimization
Zhehui Chen*, Xingguo Li*, Lin Yang, Jarvis Haupt and Tuo Zhao [arXiv]
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang and Tuo Zhao [arXiv]
International Conference on Learning Representations (ICLR), 2019Online Multiview Learning: Dropping Convexity for Better Efficiency
Zhehui Chen, Lin Yang, Chris Li, and Tuo Zhao [arXiv]
International Conference on Machine Learning (ICML), PMLR 70:777-786, 2017
Preprints
A hierarchical expected improvement method for effective Bayesian optimization
Zhehui Chen*, Simon Mak*, C.F. Jeff Wu [arXiv]
Journal of American Statistical Association (JASA) accepted.Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao
Working in Progress.