23 citations · 32 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
Amortizing intractable inference in large language models
Edward J. Hu, Moksh Jain, Eric Elmoznino +4
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits tractable querying of this knowled…
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…