This paper introduces semi-ring dictionaries, a powerful class of compositional and purely
functional collections that subsume other collection types such as sets, multisets, arrays, …

We present semantic correctness proofs of Automatic Differentiation (AD). We consider a
forward-mode AD method on a higher order language with algebraic data types, and we …

Optimizing the expected values of probabilistic processes is a central problem in computer
science and its applications, arising in fields ranging from artificial intelligence to operations …

This article presents GenSQL, a probabilistic programming system for querying probabilistic
generative models of database tables. By augmenting SQL with only a few key primitives for …

We introduce a new setting, the category of ωPAP spaces, for reasoning denotationally
about expressive differentiable and probabilistic programming languages. Our semantics is …

Sparse tensors are prevalent in many data-intensive applications. However, existing automatic
differentiation (AD) frameworks are tailored towards dense tensors, which makes it a …

Reverse-mode differentiation is used for optimization, but it introduces references, which
break the purity of the underlying programs, making them notoriously harder to optimize. We …

Compared to the wide array of advanced Monte Carlo methods supported by modern
probabilistic programming languages (PPLs), PPL support for variational inference (VI) is less …

We present semantic correctness proofs of automatic differentiation (AD). We consider a
forward-mode AD method on a higher order language with algebraic data types, and we …

We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers,
allowing us to differentiate through probabilistic inference, even if the model has discrete …