Publications

Horseshoe Priors for Sparse Dirichlet-Multinomial Models

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

Yuexi Wang, Nicholas G. Polson

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

Adversarial Bayesian Simulation

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

Yuexi Wang, Veronika Rockova

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

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