arXiv:2508. 01392v2 Announce Type: replace Abstract: Gibbs measures, such as Coulomb gases, are popular in modelling systems of interacting particles.
arXiv:2402. 11736v3 Announce Type: replace Abstract: Kernel herding belongs to a family of deterministic quadratures that seek to minimize the maximum mean discrepancy (MMD), that is, the worst-case integration error over a reproducing kernel Hilbert space (RKHS).
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...
arXiv:2409. 08469v4 Announce Type: replace-cross Abstract: We provide finite-particle convergence rates for the Stein Variational Gradient Descent (SVGD) algorithm in the Kernelized Stein Discrepancy ($\mathsf{KSD}$) and Wasserstein-2 metrics.
arXiv:2606. 15871v1 Announce Type: cross Abstract: Bayesian inference for inverse problems is run to evaluate integrals -- posterior expectations, tail probabilities, and risks -- across a stream of observations.
arXiv:2610. 01088v1 Announce Type: cross Abstract: The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of mixing distributions.
The paper investigates the Microcanonical Hamiltonian Monte Carlo (MHMC) algorithm from a thermodynamic perspective, deriving its state variables and potentials to show that it represents a microcanonical thermodynamic ensemble. It demonstrates analytically and numerically that MHMC satisfies the Helmholtz theorem, an alternative form of the first law of thermodynamics, and introduces a new sampling algorithm tailored for lower-dimensional inference problems. The authors conclude that canonical Markov Chain Monte Carlo methods are more natural than MHMC when evaluated through thermodynamic and information-theoretic lenses.
We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions. RHMC proceeds by repeatedly simulating the continuous-time Hamiltonian dynamics for some random integration times, and resetting the velocity to be an independent Gaussian random variable between each simulation.
arXiv:2607. 14527v1 Announce Type: cross Abstract: Stein variational gradient descent (SVGD) transports interacting particles toward a target distribution through deterministic kernelized dynamics.
arXiv:2607. 12902v1 Announce Type: cross Abstract: We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions.
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.
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.
arXiv:2607. 15208v1 Announce Type: cross Abstract: Unadjusted samplers such as unadjusted Hamiltonian Monte Carlo and underdamped Langevin are well-known to be biased.