arXiv Machine Learning July 14, 2026 By Michalis K. Titsias, Angelos Alexopoulos, Siran Liu, Petros Dellaportas

Gaussian Invariant Markov Chain Monte Carlo

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arXiv:2506. 21511v2 Announce Type: replace-cross Abstract: We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA.

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benchmarks

arXiv Machine Learning

Jul 2

Optimal scaling of MCMC algorithms: exploiting the symmetry of the Metropolis-Hastings formula

arXiv:2607. 00586v1 Announce Type: cross Abstract: We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases.

By P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis

arXiv Machine Learning

Jul 14

Optimal scaling of MCMC algorithms: the Hamiltonian approach

arXiv:2607. 00586v2 Announce Type: replace-cross Abstract: We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases.

By P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis

arXiv Statistics ML

Sep 4

Markov Chain Monte Carlo with Diffusion Paths

The paper introduces a new Markov chain Monte Carlo method that samples from multimodal distributions by interpolating along the diffusion path of a noising diffusion process, preserving mode weights and improving mixing. It proposes a Metropolis-adjusted diffusion path (MAD-Path) sampler that corrects for bias from approximate score estimates and discretization errors, ensuring the target distribution remains invariant. Experiments on Bayesian posteriors demonstrate that MAD-Path outperforms tempering-based MCMC and unadjusted diffusion samplers in global exploration and accurate mode-weight estimation.

By Han Chen, Sifan Liu, Jun Yang

diffusion multimodal safety

arXiv Machine Learning

Sep 24

Variance Reduction for Independent Metropolis

arXiv:2406.17699v3 Announce Type: replace-cross Abstract: Assume that we would like to estimate the expected value of a function $F$ with respect to an intractable density $\pi$, which is specified u...

By Siran Liu, Petros Dellaportas, Michalis K. Titsias

arXiv Statistics ML

2d ago

On skew-symmetric distributions and their use in Monte Carlo sampling algorithms: coordinate-free, Gibbs-style and manifold versions of the Barker proposal

arXiv:2610.01448v1 Announce Type: cross Abstract: Skew-symmetric probability distributions provide a principled mechanism for incorporating gradient information into Markov chain Monte Carlo algorith...

By Minh Vu, Samuel Livingstone, Pantelis Samartsidis

arXiv Machine Learning

Jul 9

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

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Optimal scaling of MCMC algorithms: exploiting the symmetry of the Metropolis-Hastings formula

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