Improving Stability of Langevin Thermostat for Bayesian Sampling
Research Paper#Bayesian Sampling, Machine Learning, Langevin Dynamics🔬 Research|Analyzed: Jan 3, 2026 09:23•
Published: Dec 30, 2025 23:26
•1 min read
•ArXivAnalysis
This paper addresses the stability issues of the Covariance-Controlled Adaptive Langevin (CCAdL) thermostat, a method used in Bayesian sampling for large-scale machine learning. The authors propose a modified version (mCCAdL) that improves numerical stability and accuracy compared to the original CCAdL and other stochastic gradient methods. This is significant because it allows for larger step sizes and more efficient sampling in computationally intensive Bayesian applications.
Key Takeaways
- •Proposes a modified CCAdL (mCCAdL) thermostat to improve stability.
- •mCCAdL uses a scaling and squaring method and a truncated Taylor series approximation.
- •Employs a symmetric splitting method for discretization.
- •Demonstrates improved numerical stability and accuracy compared to the original CCAdL and other methods.
- •Relevant for large-scale Bayesian sampling in machine learning.
Reference / Citation
View Original"The newly proposed mCCAdL thermostat achieves a substantial improvement in the numerical stability over the original CCAdL thermostat, while significantly outperforming popular alternative stochastic gradient methods in terms of the numerical accuracy for large-scale machine learning applications."