8 papers · 1 filter
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…
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…
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…