7 papers
Graph-Enabled Efficient Federated Bayesian Modeling
Chenyang Zhong, Shouxuan Ji, Tian Zheng
Federated Bayesian modeling requires combining evidence from distributed users into a coherent global posterior while keeping users' raw data on-device. We propose Federated Latent…
Entropic regularization of Monge's problem
Marcel Nutz, Chenyang Zhong
We study the vanishing-regularization limit of entropically regularized optimal transport (EOT) for the Euclidean distance cost in dimension . We develop a co…
A Particle Algorithm for Mean-Field Variational Inference
Qiang Du, Kaizheng Wang, Edith Zhang +1
Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A…
Variational Inference for Latent Variable Models in High Dimensions
Chenyang Zhong, Sumit Mukherjee, Bodhisattva Sen
Variational inference (VI) is a popular method for approximating intractable posterior distributions in Bayesian inference and probabilistic machine learning. In this paper, we int…
Counting the number of group orbits by marrying the Burnside process with importance sampling
Persi Diaconis, Chenyang Zhong
This paper introduces a novel and general algorithm for approximately counting the number of orbits under group actions. The method is based on combining the Burnside process and i…
Efficient Generative Modeling via Penalized Optimal Transport Network
Wenhui Sophia Lu, Chenyang Zhong, Wing Hung Wong
The generation of synthetic data with distributions that faithfully emulate the underlying data-generating mechanism holds paramount significance. Wasserstein Generative Adversaria…