Carol Mak

2 accepted papers

2022

Nonparametric Involutive Markov Chain Monte Carlo

ICML 2022oral

A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We present the nonparametric involutive Markov chain Monte Carlo (NP-iMCMC) algorithm as a method for constructing MCMC infe…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2021

Nonparametric Hamiltonian Monte Carlo

Carol Mak, Fabian Zaiser, Luke Ong

ICML 2021spotlight

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 general purpose inference algorithms that work out-of-the-box for arbitrary programs in a universal probabilistic programm…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗