On quantitative Laplace-type convergence results for some exponential probability measures, with two applications
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| Format: | Preprint |
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2021
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| author | De Bortoli, Valentin Desolneux, Agnès |
| author_facet | De Bortoli, Valentin Desolneux, Agnès |
| contents | Laplace-type results characterize the limit of sequence of measures $(π_\varepsilon)_{\varepsilon >0}$ with density w.r.t the Lebesgue measure $(\mathrm{d} π_\varepsilon / \mathrm{d} \mathrm{Leb})(x) \propto \exp[-U(x)/\varepsilon]$ when the temperature $\varepsilon>0$ converges to $0$. If a limiting distribution $π_0$ 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 $π_\varepsilon$ and $π_0$ 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2110_12922 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | On quantitative Laplace-type convergence results for some exponential probability measures, with two applications De Bortoli, Valentin Desolneux, Agnès Probability Machine Learning Laplace-type results characterize the limit of sequence of measures $(π_\varepsilon)_{\varepsilon >0}$ with density w.r.t the Lebesgue measure $(\mathrm{d} π_\varepsilon / \mathrm{d} \mathrm{Leb})(x) \propto \exp[-U(x)/\varepsilon]$ when the temperature $\varepsilon>0$ converges to $0$. If a limiting distribution $π_0$ 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 $π_\varepsilon$ and $π_0$ 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. |
| title | On quantitative Laplace-type convergence results for some exponential probability measures, with two applications |
| topic | Probability Machine Learning |
| url | https://arxiv.org/abs/2110.12922 |