7 papers
The Influence Function of Transport-based Quantiles
Alberto González-Sanz, Shunan Sheng, Bohan Wu +1
Transport-based quantiles extend univariate quantiles to multivariate distributions via optimal transport. We study the influence function of the transport quantile map $\mathbf{Q}…
Environment-Robust Representation Learning with Empirical Bayes
Yuli Slavutsky, Matthew Shen, Bohan Wu +1
We consider multi-environment prediction problems. We assume the environments change the distribution of a latent variable, while the mechanisms generating observed covariates and…
The Illusion of Learning from Observational Data: An Empirical Bayes Perspective
Bohan Wu, Sebastian Salazar, Donald P. Green +1
Randomized experiments have long been the gold standard for scientists seeking to learn about cause and effect. When randomized experiments are infeasible, scientists often resort…
Theory and computation for structured variational inference
Shunan Sheng, Bohan Wu, Bennett Zhu +2
Structured variational inference constitutes a core methodology in modern statistical applications. Unlike mean-field variational inference, the approximate posterior is assumed to…
Mode Collapse of Mean-Field Variational Inference
Shunan Sheng, Bohan Wu, Alberto González-Sanz
Mean-field variational inference (MFVI) is a widely used method for approximating high-dimensional probability distributions by product measures. It has been empirically observed t…
Stability of Mean-Field Variational Inference
Shunan Sheng, Bohan Wu, Alberto González-Sanz +1
Mean-field variational inference (MFVI) is a widely used method for approximating high-dimensional probability distributions by product measures. This paper studies the stability p…
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
Bohan Wu, David Blei
Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models. In this paper, we propose a novel VI method that extends…