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 …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is
imperative that we aggressively investigate fairness and bias in decision-making programs …

This paper presents a new static analysis for deriving upper bounds on the expected
resource consumption of probabilistic programs. The analysis is fully automatic and derives …

Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …

Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3,
TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training …

We present for the first time a supermartingale certificate for ω-regular specifications. We
leverage the Robbins & Siegmund convergence theorem to characterize supermartingale …

As machine learning systems are increasingly used to make real world legal and financial
decisions, it is of paramount importance that we develop algorithms to verify that these …