11 papers
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,…
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…