JaxSGMC: Modular stochastic gradient MCMC in JAX
Abstract
We present JaxSGMC, an application-agnostic library for stochastic gradient Markov chain Monte Carlo (SG-MCMC) in JAX. SG-MCMC schemes are uncertainty quantification (UQ) methods that scale to large datasets and high-dimensional models, enabling trustworthy neural network predictions via Bayesian deep learning. JaxSGMC implements several state-of-the-art SG-MCMC samplers to promote UQ in deep learning by reducing the barriers of entry for switching from stochastic optimization to SG-MCMC sampling. Additionally, JaxSGMC allows users to build custom samplers from standard SG-MCMC building blocks. Due to this modular structure, we anticipate that JaxSGMC will accelerate research into novel SG-MCMC schemes and facilitate their application across a broad range of domains.
Cite
@article{arxiv.2505.11190,
title = {JaxSGMC: Modular stochastic gradient MCMC in JAX},
author = {Stephan Thaler and Paul Fuchs and Ana Cukarska and Julija Zavadlav},
journal= {arXiv preprint arXiv:2505.11190},
year = {2025}
}