Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

Pyro is a probabilistic programming language built on Python as a platform for developing
advanced probabilistic models in AI research. To scale to large data sets and high …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

This work offers a broad perspective on probabilistic modeling and inference in light of
recent advances in probabilistic programming, in which models are formally expressed in …

Prompted models have demonstrated impressive few-shot learning abilities. Repeated
interactions at test-time with a single model, or the composition of multiple models together …

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 …

Bayesian Modeling and Computation in Python aims to help beginner Bayesian
practitioners to become intermediate modelers. It uses a hands on approach with PyMC3 …

We describe a simple, low-level approach for embedding probabilistic programming in a
deep learning ecosystem. In particular, we distill probabilistic programming down to a single …

We consider the problem of expected cost analysis over nondeterministic probabilistic
programs, which aims at automated methods for analyzing the resource-usage of such …