3 papers
stat.ME2026
Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning
Andrew Gerard Roberts, Michael C. Dietze, Jonathan H. Huggins
Surrogate models - also called emulators - are widely used to facilitate Bayesian inference in settings where computational costs preclude the use of standard posterior inference a…
stat.ML2025
Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms
Xiaoyu Wang, Mikolaj J. Kasprzak, Jeffrey Negrea +2
Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in…
stat.CO2024
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
Naitong Chen, Jonathan H. Huggins, Trevor Campbell
A Bayesian coreset is a small, weighted subset of a data set that replaces the full data during inference to reduce computational cost. The state-of-the-art coreset construction al…