12 citations · 27 across the 5 of their papers we have counts for
8 papers
An Efficient HPR Algorithm for the Wasserstein Barycenter Problem with Computational Complexity
Guojun Zhang, Yancheng Yuan, Defeng Sun
In this paper, we propose and analyze an efficient Halpern-Peaceman-Rachford (HPR) algorithm for solving the Wasserstein barycenter problem (WBP) with fixed supports. While the Pea…
Domain Adversarial Training: A Game Perspective
David Acuna, Marc T Law, Guojun Zhang +1
The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a…
f-Domain-Adversarial Learning: Theory and Algorithms
David Acuna, Guojun Zhang, Marc T. Law +1
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labele…
Quantifying and Improving Transferability in Domain Generalization
Guojun Zhang, Han Zhao, Yaoliang Yu +1
Out-of-distribution generalization is one of the key challenges when transferring a model from the lab to the real world. Existing efforts mostly focus on building invariant featur…
Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics
Xiwang Liu, Guojun Zhang, Jie Li +6
Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous p…
Convergence of Gradient Methods on Bilinear Zero-Sum Games
Guojun Zhang, Yaoliang Yu
Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, while understanding the dynamics of gradi…