On quantitative Laplace-type convergence results for some exponential probability measures, with two applications

arXiv stat.ML / 4/29/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

Key Points

  • The paper studies Laplace-type (small-noise) convergence for probability measures with densities proportional to exp[-U(x)/ε] as the temperature ε→0.
  • It derives quantitative Wasserstein-1 bounds between the measures π_ε and a limiting distribution π_0 without requiring the classical Hessian of U to be invertible, instead using an invertibility condition on a generalized Jacobian.
  • The analysis focuses on norm-like potentials U and relies on geometric measure theory tools, particularly the coarea formula, to support the bounds.
  • Two applications are presented: convergence properties of maximum entropy models (microcanonical/macrocanonical distributions) and low-temperature convergence behavior of the iterates of SGLD for non-convex optimization.

Abstract

Laplace-type results characterize the limit of sequence of measures (\pi_\varepsilon)_{\varepsilon >0} with density w.r.t the Lebesgue measure (\mathrm{d} \pi_\varepsilon / \mathrm{d} \mathrm{Leb})(x) \propto \exp[-U(x)/\varepsilon] when the temperature \varepsilon>0 converges to 0. If a limiting distribution \pi_0 exists, it concentrates on the minimizers of the potential U. Classical results require the invertibility of the Hessian of U in order to establish such asymptotics. In this work, we study the particular case of norm-like potentials U and establish quantitative bounds between \pi_\varepsilon and \pi_0 w.r.t. the Wasserstein distance of order 1 under an invertibility condition of a generalized Jacobian. One key element of our proof is the use of geometric measure theory tools such as the coarea formula. We apply our results to the study of maximum entropy models (microcanonical/macrocanonical distributions) and to the convergence of the iterates of the Stochastic Gradient Langevin Dynamics (SGLD) algorithm at low temperatures for non-convex minimization.