arXiv Machine Learning June 2, 2026 By Xiaoyu Wang, Jonathan H. Huggins

Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

Read the original on arXiv Machine Learning →

arXiv:2606. 00309v1 Announce Type: new Abstract: Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models.

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diffusion

arXiv Machine Learning

Jun 2

Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo

arXiv:2606. 00293v1 Announce Type: new Abstract: Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty quantification remains challenging, particularly in the practically relevant settings when the batch size is large or the model is misspecified.

By Yu Wang, Jie Ding, Jonathan H. Huggins

arXiv Statistics ML

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Kinetic Interacting Particle Langevin Monte Carlo

arXiv:2407.05790v4 Announce Type: replace-cross Abstract: This paper introduces and analyses interacting underdamped Langevin algorithms, termed Kinetic Interacting Particle Langevin Monte Carlo (KIP...

By Paul Felix Valsecchi Oliva, O. Deniz Akyildiz

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arXiv Machine Learning

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Gradient-free Riemannian Langevin Sampler

arXiv:2607. 07519v1 Announce Type: new Abstract: We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping.

By Ricardo Baptista, Olivier Zahm

multimodal benchmarks

arXiv Machine Learning

Aug 10

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement

arXiv:2507. 08390v5 Announce Type: replace Abstract: Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training.

By Meihua Dang, Jiaqi Han, Minkai Xu, Kai Xu, Akash Srivastava, Stefano Ermon

llms diffusion

arXiv Machine Learning

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HyperMC: Multi-Fidelity Hyperparameter Tuning for Stochastic Gradient MCMC

HyperMC is a multi‑fidelity hyperparameter tuning framework for stochastic gradient Markov chain Monte Carlo (SGMCMC) that combines Hyperband-style resource allocation with kernel Stein discrepancy (KSD) evaluation. It uses successive‑halving brackets to explore a continuous hyperparameter space while progressively refining promising configurations within a fixed computational budget. Robust HyperMC further introduces global grid initialization and elite‑guided local refinement to reduce sensitivity to random candidate generation and noisy evaluations, and theoretical analysis shows that the successive‑halving component selects a near‑optimal configuration with high probability under suitable conditions.

By Ming Tan, Xiyun Jiao

arXiv Machine Learning

Jun 30

Variance Reduction for Stochastic Gradient Generalized Non-reversible Langevin Monte Carlo Algorithms

arXiv:2606. 28808v1 Announce Type: cross Abstract: We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics.

By Bingye Ni, Xiaoyu Wang, Yingli Wang, Lingjiong Zhu

diffusion

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