Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …

We establish conditions under which latent causal graphs are nonparametrically identifiable
and can be reconstructed from unknown interventions in the latent space. Our primary focus …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their
responses to questions, used in fields as diverse as education, medicine and psychology …

Recent advances in deep generative modeling have enabled efficient modeling of high
dimensional data distributions and opened up a new horizon for solving data compression …

We investigate a variant of variational autoencoders where there is a superstructure of
discrete latent variables on top of the latent features. In general, our superstructure is a tree …

Stochastic variational inference offers an attractive option as a default method for
differentiable probabilistic programming. However, the performance of the variational …

Amortized variational inference (A-VI) is a method for approximating the intractable posterior
distributions that arise in probabilistic models. The defining feature of A-VI is that it learns a …

We introduce a framework for automatically defining and learning deep generative models
with problem-specific structure. We tackle problem domains that are more traditionally …