arXiv subjects

Manuel Arnese

Publications and source records attributed to Manuel Arnese.

4 recordsLinked to original sources

arXiv2026

Quantitative propagation of chaos and universality for asymmetric Langevin spin glass dynamics

We obtain quantitative estimates on quenched propagation of chaos for Langevin spin glass dynamics with i.i.d. disorder. Prior work in the case of Gaussian disorder established the qualitative convergence of the law of a single spin to a deterministic McKean-Vlasov limit. We prove convergence rates in expected Wasserstein distance and quantitative concentration rates for Lipschitz observables under the assumption that the disorder satisfies the T2 inequality. The proof uses a coupling argument, together with techniques from concentration of measure, filtering theory, and Malliavin calculus

math.PR↗

arXiv2026

Sharp propagation of chaos for mean field Langevin dynamics, control, and games

We establish the sharp rate of propagation of chaos for McKean-Vlasov equations with coefficients that are non-linear in the measure argument, i.e., not necessarily given by pairwise interactions. Results are given both on bounded time horizon and uniform in time. As applications, we deduce the sharp rate of propagation of chaos for the convergence problem in mean field games and control, and for mean field Langevin dynamics, the latter being uniform in time in the strongly displacement convex regime. Our arguments combine the BBGKY hierarchy with techniques from the literature on weak propagation of chaos.

math.PR↗

arXiv2025

Regularity and propagation of chaos for conditional McKean-Vlasov equations

We study the rate of propagation of chaos for a McKean--Vlasov equation with conditional expectation terms in the drift. We use a (regularized) Nadaraya--Watson estimator at a particle level to approximate the conditional expectations; we then combine relative entropy methods in the spirit of Jabin and Wang (2018) with information theoretic inequalities to obtain the result. The nonparametric nature of the problem requires higher regularity for the density of the McKean--Vlasov limit, which we obtain with a bootstrap argument and energy estimates.

math.PR↗

arXiv2024

Convergence of coordinate ascent variational inference for log-concave measures via optimal transport

Mean field variational inference (VI) is the problem of finding the closest product (factorized) measure, in the sense of relative entropy, to a given high-dimensional probability measure $\rho$. The well known Coordinate Ascent Variational Inference (CAVI) algorithm aims to approximate this product measure by iteratively optimizing over one coordinate (factor) at a time, which can be done explicitly. Despite its popularity, the convergence of CAVI remains poorly understood. In this paper, we prove the convergence of CAVI for log-concave densities $\rho$. If additionally $\log \rho$ has Lipschitz gradient, we find a linear rate of convergence, and if also $\rho$ is strongly log-concave, we find an exponential rate. Our analysis starts from the observation that mean field VI, while notoriously non-convex in the usual sense, is in fact displacement convex in the sense of optimal transport when $\rho$ is log-concave. This allows us to adapt techniques from the optimization literature on coordinate descent algorithms in Euclidean space.

stat.ML↗