12 papers
Exponential Family Synthetic Controls
Hector Rodriguez-Deniz, David M. Blei
We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the panel corresponds to a dataset dra…
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…
Robust Representation Learning through Explicit Environment Modeling
Yuli Slavutsky, David M. Blei
We consider learning from labeled data collected across multiple environments, where the data distribution may vary across these environments. This problem is commonly approached f…
Neural Generalized Mixed-Effects Models
Yuli Slavutsky, Sebastian Salazar, David M. Blei
Generalized linear mixed-effects models (GLMMs) are widely used to analyze grouped and hierarchical data. In a GLMM, each response is assumed to follow an exponential-family distri…
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…
Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
Bohan Wu, Julius von Kügelgen, David M. Blei
Causal representation learning (CRL) aims to learn low-dimensional causal latent variables from high-dimensional observations. While identifiability has been extensively studied fo…
Worst-case low-rank approximations
Anya Fries, Markus Reichstein, David Blei +1
Real-world data in health, economics, and environmental sciences are often collected across heterogeneous domains (such as hospitals, regions, or time periods). In such settings, d…
Input Adaptive Bayesian Model Averaging
Yuli Slavutsky, Sebastian Salazar, David M. Blei
This paper studies prediction with multiple candidate models, where the goal is to combine their outputs. This task is especially challenging in heterogeneous settings, where diffe…
Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
Neural networks make accurate predictions but often fail to provide reliable uncertainty estimates, especially under covariate distribution shifts between training and testing. To…
Variational Learning of Disentangled Representations
Yuli Slavutsky, Ozgur Beker, David Blei +1
Disentangled representations separate factors that are shared across conditions from those that are condition-specific. Such separation is needed for generalization to new domains,…
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference
Sebastian Salazar, Michal Kucer, Yixin Wang +2
This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model…
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…