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In many applications of machine learning, a large number of variables are considered. Motivated by machine learning of interacting particle systems, we consider the situation when the number of input variables goes to infinity. First, we…

Machine Learning · Computer Science 2023-10-30 Christian Fiedler , Michael Herty , Sebastian Trimpe

We obtain rates of convergence in the weak invariance principle (functional central limit theorem) for $\R^d$-valued H\"older observables of nonuniformly hyperbolic maps. In particular, for maps modelled by a Young tower with…

Dynamical Systems · Mathematics 2025-03-21 Nicholas Fleming-Vázquez

Most state-of-the-art graph kernels only take local graph properties into account, i.e., the kernel is computed with regard to properties of the neighborhood of vertices or other small substructures. On the other hand, kernels that do take…

Machine Learning · Computer Science 2017-09-25 Christopher Morris , Kristian Kersting , Petra Mutzel

We discuss the geometry behind some integrals related to structure constants of the Liouville conformal field theory.

High Energy Physics - Theory · Physics 2021-04-23 Vadim Schechtman

We provide rates of convergence in the central limit theorem in terms of projective criteria for adapted stationary sequences of centered random variables taking values in Banach spaces, with finite moment of order $p \in ]2,3]$ as soon as…

Probability · Mathematics 2025-02-21 Aurélie Bigot

We consider the Monge problem of optimal transport between a compactly supported source measure and a target probability measure with unbounded support. We consider the convergence of optimal maps and potential functions when the target…

Numerical Analysis · Mathematics 2026-03-03 Axel G. R. Turnquist

We develop kernel criteria for the likelihood-ratio, hazard-rate, usual stochastic, and relative log-concavity orders in parametric families of univariate probability laws with densities. The score is the derivative of the log density with…

Probability · Mathematics 2026-05-19 Zakaria Derbazi

Kernel dependence measures yield accurate estimates of nonlinear relations between random variables, and they are also endorsed with solid theoretical properties and convergence rates. Besides, the empirical estimates are easy to compute in…

Machine Learning · Statistics 2016-11-03 Adrián Pérez-Suay , Gustau Camps-Valls

Let $M$ be a complex manifold with boundary $X$, which admits a holomorphic Lie group $G$-action preserving $X$. We establish a full asymptotic expansion for the $G$-invariant Bergman kernel under certain assumptions. As an application, we…

Complex Variables · Mathematics 2024-04-25 Chin-Yu Hsiao , Rung-Tzung Huang , Xiaoshan Li , Guokuan Shao

In this article we propose a general framework for normal approximation using Stein's method. We introduce the new concept of Stein couplings and we show that it lies at the heart of popular approaches such as the local approach,…

Probability · Mathematics 2010-10-27 Louis H. Y. Chen , Adrian Röllin

The isotropic 4-wave kinetic equation is considered in its weak formulation using model (simplified) homogeneous kernels. Existence and uniqueness of solutions is proven in a particular setting where the kernels have a rate of growth at…

Probability · Mathematics 2016-12-21 Sara Merino-Aceituno

New index transforms with Weber type kernels, consisting of products of Bessel functions of the first and second kind are investigated. Mapping properties and inversion formulas are established for these transforms in Lebesgue spaces. The…

Classical Analysis and ODEs · Mathematics 2018-01-08 Semyon Yakubovich

This is a continuation of paper \cite{Li}. On any toric Fano manifold, we discuss the behavior of limit metric of a sequence of metrics, which are solutions to a continuity family of complex Monge-Ampere equations in Kahler-Einstein…

Differential Geometry · Mathematics 2019-02-20 Chi Li

We give a new proof of the classical Central Limit Theorem, in the Mallows ($L^r$-Wasserstein) distance. Our proof is elementary in the sense that it does not require complex analysis, but rather makes use of a simple subadditive inequality…

Probability · Mathematics 2007-06-13 Oliver Johnson , Richard Samworth

A kernel method is proposed to estimate the condensed density of the generalized eigenvalues of pencils of Hankel matrices whose elements have a joint noncentral Gaussian distribution with nonidentical covariance. These pencils arise when…

Statistics Theory · Mathematics 2015-10-02 Piero Barone

We study random transformations built from intermittent maps on the unit interval that share a common neutral fixed point. We focus mainly on random selections of Pomeu-Manneville-type maps $T_\alpha$ using the full parameter range $0<…

Dynamical Systems · Mathematics 2016-08-11 Wael Bahsoun , Christopher Bose

The phenomenon of turbulence is investigated in the context of globally coupled maps. The local dynamics is given by the Chat\'e-Manneville minimal map previously used in studies of spatiotemporal intermittency in locally coupled map…

chao-dyn · Physics 2009-10-28 M. G. Cosenza , A. Parravano

Focusing on the continuum meson bound-state problem, a novel method is used to calculate closed-form Bethe-Salpeter kernels that are symmetry consistent with any reasonable gluon-quark vertex, $\Gamma_\nu$, and therewith deliver a…

High Energy Physics - Phenomenology · Physics 2023-03-22 Zhen-Ni Xu , Zhao-Qian Yao , Si-Xue Qin , Zhu-Fang Cui , Craig D. Roberts

Stein's method is used to prove limit theorems for random character ratios. Tools are developed for four types of structures: finite groups, Gelfand pairs, twisted Gelfand pairs, and association schemes. As one example an error term is…

Combinatorics · Mathematics 2007-05-23 Jason Fulman

A kernel of a directed graph is a subset of vertices that is both independent and absorbing (every vertex not in the kernel has an out-neighbour in the kernel). Not all directed graphs contain kernels, and computing a kernel or deciding…

Discrete Mathematics · Computer Science 2024-05-20 Bruno Jartoux