Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures

Manuel Saenz, Jean Barbier, Dmitry Panchenko

Abstract: We consider a generic class of log-concave, possibly random, (Gibbs) measures. We prove the concentration of an infinite family of order parameters called multioverlaps. Because they completely parametrize the quenched Gibbs measure of the system, this implies a simple representation of the asymptotic Gibbs measures, as well as the decoupling of the variables in a strong sense. These results may prove themselves useful in several contexts. In particular in machine learning and high-dimensional inference, log-concave measures appear in convex empirical risk minimization, maximum a-posteriori inference or M-estimation. We believe that they may be applicable in establishing some type of ‘replica symmetric formulas’ for the free energy, inference or generalization error in such settings.