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 …

Injecting structure into neural networks enables learning functions that satisfy invariances
with respect to subsets of inputs. For instance, when learning generative models using …

Normalizing flows model complex probability distributions by combining a base distribution
with a series of bijective neural networks. State-of-the-art architectures rely on coupling and …

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

Abstract Stochastic gradient Markov Chain Monte Carlo (SGMCMC) is a scalable algorithm
for asymptotically exact Bayesian inference in parameter-rich models, such as Bayesian …

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

Amortized Bayesian inference trains neural networks to solve stochastic inference problems
using model simulations, thereby making it possible to rapidly perform Bayesian inference …

The automation of probabilistic reasoning is one of the primary aims of machine learning.
Recently, the confluence of variational inference and deep learning has led to powerful and …

In this work, we provide a deterministic alternative to the stochastic variational training of
generative autoencoders. We refer to these new generative autoencoders as AutoEncoders …

In probabilistic programming languages (PPLs), a critical step in optimization-based
inference methods is constructing, for a given model program, a trainable guide program …