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The method to derive uniform bounds with Gaussian and Rademacher complexities is extended to the case where the sample average is replaced by a nonlinear statistic. Tight bounds are obtained for U-statistics, smoothened L-statistics and…

Statistics Theory · Mathematics 2019-05-13 Andreas Maurer , Massimiliano Pontil

We study the complexity of Non-Gaussian Component Analysis (NGCA) in the Statistical Query (SQ) model. Prior work developed a general methodology to prove SQ lower bounds for this task that have been applicable to a wide range of contexts.…

Machine Learning · Computer Science 2024-03-08 Ilias Diakonikolas , Daniel Kane , Lisheng Ren , Yuxin Sun

Concentration inequalities form an essential toolkit in the study of high dimensional (HD) statistical methods. Most of the relevant statistics literature in this regard is based on sub-Gaussian or sub-exponential tail assumptions. In this…

Statistics Theory · Mathematics 2023-01-09 Arun Kumar Kuchibhotla , Abhishek Chakrabortty

We consider the statistical inverse problem of recovering a parameter $\theta\in H^\alpha$ from data arising from the Gaussian regression problem \begin{equation*} Y = \mathscr{G}(\theta)(Z)+\varepsilon \end{equation*} with nonlinear…

Statistics Theory · Mathematics 2025-09-30 Maximilian Siebel

We study the Hardy inequality when the singularity is placed on the boundary of a bounded domain in $\mathbb{R}^n$ that satisfies both an interior and exterior ball condition at the singularity. We obtain the sharp Hardy constant $n^2/4$ in…

Analysis of PDEs · Mathematics 2018-04-06 Gerassimos Barbatis , Stathis Filippas , Achilles Tertikas

We show that the law of the $\gamma$-LQG metric (appropriately renormalized) is continuous in $\gamma \in (0,2)$ with respect to the local uniform topology of metrics on $\mathbf{C} \times \mathbf{C}$ whenever $\gamma$ lies on compact…

Probability · Mathematics 2025-09-22 Konstantinos Kavvadias

We present the current status of our computation of quark bilinear renormalization constants for Wilson fermions and Symanzik improved gauge action. Computations are performed in Numerical Stochastic Perturbation Theory. Volumes range from…

High Energy Physics - Lattice · Physics 2010-02-03 M. Brambilla , F. Di Renzo , L. Scorzato

The aim of this paper is to characterize the so-called Hardy means, i.e., those means $M\colon\bigcup_{n=1}^\infty \mathbb{R}_+^n\to\mathbb{R}_+$ that satisfy the inequality $$ \sum_{n=1}^\infty M(x_1,\dots,x_n) \le C\sum_{n=1}^\infty x_n…

Classical Analysis and ODEs · Mathematics 2018-03-20 Zsolt Páles , Paweł Pasteczka

We compute quantitative bounds for measuring the discrepancy between the distribution of two min-max statistics involving either pairs of Gaussian random matrices, or one Gaussian and one Gaussian-subordinated random matrix. In the fully…

Probability · Mathematics 2021-09-28 Giovanni Peccati , Nicola Turchi

The large-matrix limit laws of the rescaled largest eigenvalue of the orthogonal, unitary, and symplectic $n$-dimensional Gaussian ensembles -- and of the corresponding Laguerre ensembles (Wishart distributions) for various regimes of the…

Probability · Mathematics 2026-04-09 Folkmar Bornemann

This article study the fractional Hamiltonian systems \begin{eqnarray}\label{00} {_{t}}D_{\infty}^{\alpha}({_{-\infty}}D_{t}^{\alpha}u) + \lambda L(t)u = \nabla W(t, u), \;\;t\in \mathbb{R}, \end{eqnarray} where $\alpha \in (1/2, 1)$,…

Analysis of PDEs · Mathematics 2015-03-25 César E. Torres Ledesma

We study high-dimensional Laplace-type integrals $I(\lambda):=(\lambda/2\pi)^{d/2}\int_{\mathbb R^d} g(x)e^{-\lambda f(x)}dx$ in the regime where both $d$ and $\lambda$ are large. Existing rigorous Laplace-expansion results in growing…

Classical Analysis and ODEs · Mathematics 2026-03-13 Alexander Katsevich , Anya Katsevich

In this paper, we consider an unconstrained optimization model where the objective is a sum of a large number of possibly nonconvex functions, though overall the objective is assumed to be smooth and convex. Our bid to solving such model…

Optimization and Control · Mathematics 2022-03-15 Xi Chen , Bo Jiang , Tianyi Lin , Shuzhong Zhang

We prove an exponential decay concentration inequality to bound the tail probability of the difference between the log-likelihood of discrete random variables on a finite alphabet and the negative entropy. The concentration bound we derive…

Probability · Mathematics 2021-06-23 Yunpeng Zhao

We present several new phenomena about almost sure convergence on homogeneous chaoses that include Gaussian Wiener chaos and homogeneous sums in independent random variables. Concretely, we establish the fact that almost sure convergence on…

Probability · Mathematics 2019-02-25 Guillaume Poly , Guangqu Zheng

Classical Edgeworth expansions provide asymptotic correction terms to the Central Limit Theorem (CLT) up to an order that depends on the number of moments available. In this paper, we provide subsequent correction terms beyond those given…

Probability · Mathematics 2011-03-23 Henry Lam , Jose Blanchet , Damian Burch , Martin Z. Bazant

Consider the set of all sequences of $n$ outcomes, each taking one of $m$ values, that satisfy a number of linear constraints. If $m$ is fixed while $n$ increases, most sequences that satisfy the constraints result in frequency vectors…

Information Theory · Computer Science 2016-11-18 Kostas N. Oikonomou , Peter D. Grunwald

In this paper, we develop a stochastic algorithm based on the Euler--Maruyama scheme to approximate the invariant measure of the limiting multidimensional diffusion of $G/Ph/n+GI$ queues in the Halfin-Whitt regime. Specifically, we prove a…

Probability · Mathematics 2022-09-16 Xinghu Jin , Guodong Pang , Lihu Xu , Xin Xu

In a recent paper, Gaunt 2020 extended Stein's method to limit distributions that can be represented as a function $g:\mathbb{R}^d\rightarrow\mathbb{R}$ of a centered multivariate normal random vector $\Sigma^{1/2}\mathbf{Z}$ with…

Probability · Mathematics 2022-09-21 Robert E. Gaunt , Heather Sutcliffe

We study a class of robust mean estimators $\widehat{\mu}$ obtained by adaptively shrinking the weights of sample points far from a base estimator $\widehat{\kappa}$. Given a data-dependent scaling factor $\widehat{\alpha}$ and a weighting…

Statistics Theory · Mathematics 2025-12-17 Antônio Catão , Lucas Resende , Paulo Orenstein
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