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We prove that the scaling limits of spin fluctuations in four-dimensional Ising-type models with nearest-neighbor ferromagnetic interaction at or near the critical point are Gaussian. A similar statement is proven for the $\lambda \phi^4$…

Mathematical Physics · Physics 2022-01-25 Michael Aizenman , Hugo Duminil-Copin

Recently, Benzoni--Gavage, Danchin, Descombes, and Jamet have given a sufficient condition for linear and nonlinear stability of solitary wave solutions of Korteweg's model for phase-transitional isentropic gas dynamics in terms of…

Analysis of PDEs · Mathematics 2007-05-23 K. Zumbrun

We consider the Graph Ornstein-Uhlenbeck (GrOU) process observed on a non-uniform discrete time grid and introduce discretised maximum likelihood estimators with parameters specific to the whole graph or specific to each component, or node.…

Methodology · Statistics 2022-07-12 Valentin Courgeau , Almut E. D. Veraart

We investigate confined L\'{e}vy flights under premises of the principle of detailed balance. The master equation admits a transformation to L\'{e}vy - Schr\"{o}dinger semigroup dynamics (akin to a mapping of the Fokker-Planck equation into…

Statistical Mechanics · Physics 2015-05-28 Piotr Garbaczewski , Vladimir Stephanovich

We study Talagrand concentration and Poincar\'e type inequalities for unbounded pure jump Markov processes. In particular we focus on processes with degenerate jumps that depend on the past of the whole system, based on the model introduced…

Probability · Mathematics 2019-04-16 Pierre Hodara , Ioannis Papageorgiou

Some scales of spaces of ultra-differentiable functions are introduced, having good stability properties with respect to infinitely many derivatives and compositions. They are well-suited for solving non-linear functional equations by means…

Dynamical Systems · Mathematics 2017-10-04 Abed Bounemoura , Jacques Féjoz

We provide simple and constructive proofs of Harris-type theorems on the existence and uniqueness of an equilibrium and the speed of equilibration of discrete-time and continuous-time stochastic semigroups. Our results apply both to cases…

Probability · Mathematics 2021-10-20 José A. Cañizo , Stéphane Mischler

We study the scaling limits of stochastic gradient descent (SGD) with constant step-size in the high-dimensional regime. We prove limit theorems for the trajectories of summary statistics (i.e., finite-dimensional functions) of SGD as the…

Machine Learning · Statistics 2023-08-21 Gerard Ben Arous , Reza Gheissari , Aukosh Jagannath

We present a field theoretical model of point-form dynamics which exhibits resonance scattering. In particular, we construct point-form Poincar\'e generators explicitly from field operators and show that in the vector spaces for the…

Mathematical Physics · Physics 2013-10-07 M. Gadella , F. Gómez-Cubillo , L. Rodriguez , S. Wickramasekara

Given a second order partial differential operator $L$ satisfying the strong H\"ormander condition with corresponding heat semigroup $P_t$, we give two different stochastic representations of $dP_t f$ for a bounded smooth function $f$. We…

Differential Geometry · Mathematics 2017-08-17 Erlend Grong , Anton Thalmaier

In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle model. In this model, an estimator has access to noisy values…

Machine Learning · Computer Science 2021-07-07 Abdulrahman Alabdulkareem , Jean Honorio

We propose an exact technique to calculate lower bounds of spectral gaps of discrete time reversible Markov chains on finite state sets. Spectral gaps are a common tool for evaluating convergence rates of Markov chains. As an illustration,…

Statistical Mechanics · Physics 2016-08-31 N. Destainville

We give a direct construction of invariant measures and global flows for the stochastic quantization equation to the quantum field theoretical $\Phi ^4_3$-model on the $3$-dimensional torus. This stochastic equation belongs to a class of…

Probability · Mathematics 2021-01-26 Sergio Albeverio , Seiichiro Kusuoka

We derive estimates relating the values of a solution at any two points to the distance between the points, for quasilinear isotropic elliptic equations on compact Riemannian manifolds, depending only on dimension and a lower bound for the…

Differential Geometry · Mathematics 2019-05-07 Ben Andrews , Changwei Xiong

By works of Schoen-Yau and Gromov-Lawson any Riemannian manifold with nonnegative scalar curvature and diffeomorphic to a torus is isometric to a flat torus. Gromov conjectured subconvergence of tori with respect to a weak Sobolev type…

Differential Geometry · Mathematics 2020-06-29 Armando J. Cabrera Pacheco , Christian Ketterer , Raquel Perales

We derive exponential bounds on probabilities of large deviations for "light tail" martingales taking values in finite-dimensional normed spaces. Our primary emphasis is on the case where the bounds are dimension-independent or nearly so.…

Probability · Mathematics 2023-01-31 Anatoli Juditsky , Arkadii S. Nemirovski

We prove that the noncommutative Lorentz norm (associated to a semifinite von Neumann algebra) of a propagator of the form $\varphi(|\mathscr{L}|)$ can be estimated if the modulus of the Borel function $\varphi$ is bounded by a continuous…

Analysis of PDEs · Mathematics 2026-02-18 Santiago Gómez Cobos , Joel E. Restrepo , Michael Ruzhansky

We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Markov chains. We apply these results to analyze the performance of the Temporal Difference (TD)…

Machine Learning · Statistics 2026-05-22 Weichen Wu , Yuting Wei , Alessandro Rinaldo

Li-Vogelius and Li-Nirenberg gave a gradient estimate for solutions of strongly elliptic equations and systems of divergence forms with piecewise smooth coefficients, respectively. The discontinuities of the coefficients are assumed to be…

Analysis of PDEs · Mathematics 2011-03-09 Jishan Fan , Kyoungsun Kim , Sei Nagayasu , Gen Nakamura

We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a…

Machine Learning · Computer Science 2021-06-08 Will Grathwohl , Kevin Swersky , Milad Hashemi , David Duvenaud , Chris J. Maddison