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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.


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.

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.

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