Skip to main content

Yuexi Wang

Assistant Professor

Research Interests

My research focuses on Bayesian theory and methodology, and their intersection with deep learning. In particular, my recent work examines simulation-based inference with generative AI models.

Education

Econometrics and Statistics, PhD, University of Chicago Booth School of Business

Additional Campus Affiliations

Assistant Professor, Statistics

Recent Publications

Wang, Y., Polson, N., & Sokolov, V. O. (2023). Data Augmentation for Bayesian Deep Learning. Bayesian Analysis, 18(4), 1041-1069. https://doi.org/10.1214/22-BA1331

Wang, Y., Kaji, T., & Rockova, V. (2022). Approximate Bayesian Computation via Classification. Journal of Machine Learning Research, 23, Article 350.

Liu, Y., Ročková, V., & Wang, Y. (2021). Variable selection with ABC Bayesian forests. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 83(3), 453-481. https://doi.org/10.1111/rssb.12423

Wang, Y., & Ročková, V. (2020). Uncertainty Quantification for Sparse Deep Learning. Proceedings of Machine Learning Research, 108, 298-308.

View all publications on Illinois Experts