Preprints

• B. Wu, S. Salazar, D. P. Green, and D. M. Blei.
  The Illusion of Learning from Observational Data: An Empirical Bayes Perspective
  arXiv preprint, 2026.
  pdf
• B. Wu, J. von Kügelgen, and D. M. Blei.
  Multi-Domain Empirical Bayes for Linearly Mixed Causal Representations
  arXiv preprint, 2026.
  pdf
• B. Wu, E. Weinstein, and D. Blei.
  Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
  arXiv preprint, 2025.
  pdf
• S. Sheng*, B. Wu*, B. Zhu, S. Chewi, and A.-A. Pooladian.
  Theory and Computation for Structured Variational Inference
  arXiv preprint, 2025.
  pdf
• S. Sheng, B. Wu, and A. González Sanz.
  Mode Collapse of Mean-Field Variational Inference
  arXiv preprint, 2025.
  pdf
• S. Sheng, B. Wu, and A. González Sanz. and M.Nutz
  Stability of Mean-Field Variational Inferenc
  arXiv preprint, 2025.
  arXiv
• D. R. Kowal and B. Wu.
  Semiparametric Discrete Data Regression with Monte Carlo Inference and Prediction
  arXiv preprint, 2021.
  arXiv

Publications

• B. Wu*, E. N. Weinstein*, Y. Wang, and D. Blei.
  Adaptive Nonparametric Perturbations of Parametric Bayesian Models with Generalized Bayes
  Journal of Machine Learning Research (to appear) , 2026
  arXiv
• B. Wu and D. Blei.
  Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
  Journal of Machine Learning Research, 2026
  pdf
• B. Wu and C. A. Uribe.
  Frequentist Guarantees of Distributed (Non)-Bayesian Inference
  Journal of Machine Learning Research, 2025.
  pdf
• D. R. Kowal and B. Wu.
  Monte Carlo Inference for Semiparametric Bayesian Regression
  Journal of the American Statistical Association, 2025.
  pdf
• B. Wu, B. Zhu, and D. Blei.
  Distributionally Robust Posterior Sampling: A Variational Bayes Approach
  ICLR Workshop on Frontiers in Probabilistic Inference: Learning Meets Sampling, 2025 (Oral Presentation).
  pdf
• D. R. Kowal and B. Wu.
  Semiparametric Count Data Regression for Self-Reported Mental Health
  Biometrics, 2023.
  pdf