4 citations · 6 across the 5 of their papers we have counts for
5 papers
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches t…
Nonparametric Involutive Markov Chain Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We p…
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
Raven Beutner, Luke Ong, Fabian Zaiser
We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-b…
Nonparametric Hamiltonian Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. A challenging goal is to develop ge…
Weak and Nested Class Memory Automata
Conrad Cotton-Barratt, Andrzej Murawski, Luke Ong
Automata over infinite alphabets have recently come to be studied extensively as potentially useful tools for solving problems in verification and database theory. One popular mode…