490 citations · 1k across the 74 of their papers we have counts for
10 papers · 1 filter
Incremental Computation for Efficient Programmable Inference in Probabilistic Programs
Fabian Zaiser, Jack Czenszak, Martin C. Rinard +2
Inference in probabilistic programs generally requires evaluating many possible program executions to find those of high posterior density. To scale inference to large datasets, it…
Probabilistic Programming with Programmable Variational Inference
McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang +4
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 develo…
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables
Mathieu Huot, Matin Ghavami, Alexander K. Lew +6
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 f…
PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka +1
We introduce a new setting, the category of PAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages. Our semantics is ge…
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
Alexander K. Lew, Mathieu Huot, Sam Staton +1
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 o…
Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages
Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka
Automatic differentiation (AD) aims to compute derivatives of user-defined functions, but in Turing-complete languages, this simple specification does not fully capture AD's behavi…