Publications

Generative Bayesian Inference with GANs

In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. …

Yuexi Wang, Veronika Rockova

Likelihood-free Inference via Structured Score Matching

In many statistical problems, the data distribution is specified through a generative process for which the likelihood function is …

Haoyu Jiang, Yuexi Wang, Yun Yang

Simulation-based Inference via Langevin Dynamics with Score Matching

Simulation-based inference (SBI) enables Bayesian analysis when the likelihood is intractable but model simulations are available. …

Haoyu Jiang, Yuexi Wang, Yun Yang

Optimal Transport-Based Generative Models for Bayesian Posterior Sampling

We investigate the problem of sampling from posterior distributions with intractable normalizing constants in Bayesian inference. Our …

Ke Li, Wei Han, Yuexi Wang, Yun Yang

Pochhammer Priors for Sparse Count Models

Bayesian inference for Dirichlet-Multinomial (DM) models has a long and important history. The concentration parameter $\alpha$ is …

Yuexi Wang, Nicholas G. Polson

Data Augmentation for Bayesian Deep Learning

Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the …

Yuexi Wang, Nicholas G Polson, Vadim Sokolov

Approximate Bayesian Computation via Classification

Approximate Bayesian Computation (ABC) enables statistical inference in simulator-based models whose likelihoods are difficult to …

Yuexi Wang, Tetsuya Kaji, Veronika Rockova

Variable Selection with ABC Bayesian Forests

Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable …

Yi Liu, Veronika Rockova, Yuexi Wang

Uncertainty Quantification for Sparse Deep Learning

Deep learning methods continue to have a decided impact on machine learning, both in theory and in practice. Statistical theoretical …

Yuexi Wang, Veronika Rockova