arXiv:2609.06906v1 Announce Type: cross Abstract: We develop a new low-accuracy sampler, called \emph{smoothed Picard Hamiltonian Monte Carlo}, which combines Gaussian smoothing, Picard iteration, an...
arXiv:2610.16248v1 Announce Type: cross Abstract: We study sampling from $\pi(x)\propto\mathrm{e}^{-V(x)}$ on $\mathbb{R}^d$, where $V$ is $\alpha$-strongly convex and $\beta$-smooth with condition n...
arXiv:2607. 28413v1 Announce Type: cross Abstract: Let $\mu(d x)\propto e^{-U(x)} d x$ on $\R^d$, where $U$ is $m$-strongly convex and $L$-smooth, and denote by $\kappa=L/m$ the condition number.
arXiv:2605. 12461v2 Announce Type: replace-cross Abstract: We propose an algorithm to sample from composite log-concave distributions over $\mathbb{R}^d$, i.
arXiv:2609. 12590v1 Announce Type: cross Abstract: We investigate the stochastic-gradient query complexity of sampling smooth strongly log-concave distributions in any fixed Euclidean dimension.
arXiv:2512. 24152v2 Announce Type: replace-cross Abstract: Sampling based on score diffusions has led to striking empirical results, and has attracted considerable attention from various research communities.
arXiv:2609.40193v1 Announce Type: new Abstract: We establish near-linear accuracy bounds for the classical Moreau--Yosida unadjusted Langevin algorithm (MYULA). The target is $\pi\propto e^{-f-g}$, w...
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:2509. 03734v3 Announce Type: replace-cross Abstract: In the hypothesis selection problem, we are given sample and query access to finite set of candidate distributions (hypotheses), $\mathcal{H} = \{H_1, \ldots, H_n\}$, and samples from an unknown distribution $P$, both over a domain $\mathcal{X}$.
arXiv:2608.24527v1 Announce Type: cross Abstract: We prove the first quantum--classical separation for a sampling problem over a continuous domain. For a class of Gibbs states $p\propto e^{-\beta E}$...
The paper investigates whether momentum-based acceleration can improve Gaussian variational inference (GVI) for strongly log-concave, log-smooth targets. With exact Gaussian expectations, GVI achieves the classic accelerated √κ convergence rate, but when only pointwise derivatives are available, the authors construct targets that force any randomized algorithm to use κ^{1-o(1)} queries, showing curvature alone does not guarantee acceleration. They identify additional structure—specifically a Hessian-Lipschitz condition with small Hessian variation—under which variance-reduced stochastic methods can achieve accelerated guarantees, and demonstrate empirical speedups on real datasets.
arXiv:2609.15884v1 Announce Type: cross Abstract: We show that logconcave probability measures along the Gaussian cooling path have thin-shell stability, generalizing the thin-shell theorem. This res...