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Title: Mean Field Approximations via Log-Concavity
Abstract

We propose a new approach to deriving quantitative mean field approximations for any probability measure $P$ on $\mathbb {R}^{n}$ with density proportional to $e^{f(x)}$, for $f$ strongly concave. We bound the mean field approximation for the log partition function $\log \int e^{f(x)}dx$ in terms of $\sum _{i \neq j}\mathbb {E}_{Q^{*}}|\partial _{ij}f|^{2}$, for a semi-explicit probability measure $Q^{*}$ characterized as the unique mean field optimizer, or equivalently as the minimizer of the relative entropy $H(\cdot \,|\,P)$ over product measures. This notably does not involve metric-entropy or gradient-complexity concepts which are common in prior work on nonlinear large deviations. Three implications are discussed, in the contexts of continuous Gibbs measures on large graphs, high-dimensional Bayesian linear regression, and the construction of decentralized near-optimizers in high-dimensional stochastic control problems. Our arguments are based primarily on functional inequalities and the notion of displacement convexity from optimal transport.

 
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Award ID(s):
2045328
PAR ID:
10333535
Author(s) / Creator(s):
; ;
Publisher / Repository:
International Mathematics Research Notices
Date Published:
Journal Name:
International Mathematics Research Notices
Volume:
2024
Issue:
7
ISSN:
1073-7928
Page Range / eLocation ID:
2206.01260
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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