3 citations · 4 across the 6 of their papers we have counts for
7 papers
Compositional Information Geometry of Probabilistic Programs
Jacek Karwowski, Frank Nielsen, Sam Staton
We study the compositional, intensional information geometry of probabilistic programs. For a first-order probabilistic programming language with finite random variables, we constr…
LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs
Maria-Nicoleta Crăciun, C. -H. Luke Ong, Tom Schrijvers +1
Hamiltonian Monte Carlo (HMC) is a successful generic inference method in probabilistic programming, but in its ordinary formulation it needs gradients and finite-dimensional param…
Modular abstract syntax trees (MAST): substitution tensors with second-class sorts
Marcelo P. Fiore, Ohad Kammar, Georg Moser +1
We adapt Fiore, Plotkin, and Turi's treatment of abstract syntax with binding, substitution, and holes to account for languages with second-class sorts. These situations include pr…
Compositional imprecise probability
Jack Liell-Cock, Sam Staton
Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programm…
Probabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets
Nathanael L. Ackerman, Cameron E. Freer, Younesse Kaddar +5
We study semantic models of probabilistic programming languages over graphs, and establish a connection to graphons from graph theory and combinatorics. We show that every well-beh…
Proceedings of the Sixth International Conference on Applied Category Theory 2023
Sam Staton, Christina Vasilakopoulou
The Sixth International Conference on Applied Category Theory took place at the University of Maryland, 31 July -- 4 August 2023. This conference follows the previous meetings at L…
A model of stochastic memoization and name generation in probabilistic programming: categorical semantics via monads on presheaf categories
Younesse Kaddar, Sam Staton
Stochastic memoization is a higher-order construct of probabilistic programming languages that is key in Bayesian nonparametrics, a modular approach that allows us to extend models…