JaxSGMC: Modular stochastic gradient MCMC in JAX

Stephan Thaler; Paul Fuchs; Ana Cukarska; Julija Zavadlav

doi:10.1016/j.softx.2024.101722

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arXiv · 2505.11190

JaxSGMC: Modular stochastic gradient MCMC in JAX

Stephan Thaler·Paul Fuchs·Ana Cukarska·Julija Zavadlav

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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.

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BibTeX RIS

Stephan Thaler, Paul Fuchs, Ana Cukarska, Julija Zavadlav. 2025-05-16. JaxSGMC: Modular stochastic gradient MCMC in JAX. https://doi.org/10.1016/j.softx.2024.101722

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