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The Hanson-Wright inequality is an upper bound for tails of real quadratic forms in independent random variables. In this work, we extend the Hanson-Wright inequality for the Ky Fan k-norm for the polynomial function of the quadratic sum of…

Probability · Mathematics 2022-03-02 Shih Yu Chang

Tackling output sampling noise due to finite shots of quantum measurement is an unavoidable challenge when extracting information in machine learning with physical systems. A technique called Eigentask Learning was developed recently as a…

Quantum Physics · Physics 2024-10-30 Fangjun Hu , Xun Gao

Convex and nonconvex finite-sum minimization arises in many scientific computing and machine learning applications. Recently, first-order and second-order methods where objective functions, gradients and Hessians are approximated by…

Optimization and Control · Mathematics 2020-05-12 Stefania Bellavia , Natasa Krejic , Benedetta Morini

On the sets of $2\pi$-periodic functions $f$, which are defined with a help of $(\psi, \beta)$-integrals of the functions $\varphi$ from $L_{1}$, we establish Lebesgue-type inequalities, in which the uniform norms of deviations of Fourier…

Classical Analysis and ODEs · Mathematics 2023-01-06 Anatoly Serdyuk , Tetiana Stepaniuk

The sampling effect of the imaging acquisition device is long considered to be a modulation process of the input signal, introducing additional error into the signal acquisition process. This paper proposes a correction algorithm for the…

Instrumentation and Methods for Astrophysics · Physics 2022-07-04 Yunqi Sun , Jianfeng Zhou

Stochastic processes are often represented through orthonormal series expansions, a framework originating in the classical works of Lo\`eve and Karhunen and widely used for simulation and numerical approximation. While truncation error in…

Statistics Theory · Mathematics 2026-03-30 Oleksandr Mokliachuk

We study fluctuations of mean-field interacting particle systems around their McKean--Vlasov limit. Our main result provides a uniform-in-time quantitative central limit theorem for the fluctuation process, with convergence rate of order…

Probability · Mathematics 2026-05-06 Solesne Bourguin , Konstantinos Spiliopoulos

Let $\mathbb{T}^d$ denote the $d$-dimensional torus. We consider the problem of optimally recovering a target function $f^*:\mathbb{T}^d\rightarrow \mathbb{C}$ from samples of its Fourier coefficients. We make classical smoothness…

Functional Analysis · Mathematics 2025-09-01 Jonathan W. Siegel

Discretization of the uniform norm of functions from a given finite dimensional subspace of continuous functions is studied. Previous known results show that for any $N$-dimensional subspace of the space of continuous functions it is…

Functional Analysis · Mathematics 2024-05-08 E. D. Kosov , V. N. Temlyakov

We consider time-dependent dynamical systems arising as sequential compositions of self-maps of a probability space. We establish conditions under which the Birkhoff sums for multivariate observations, given a centering and a general…

Dynamical Systems · Mathematics 2020-10-28 Juho Leppänen , Mikko Stenlund

Non-uniform sampling arises when an experimenter does not have full control over the sampling characteristics of the process under investigation. Moreover, it is introduced intentionally in algorithms such as Bayesian optimization and…

Machine Learning · Statistics 2020-07-03 Stijn de Waele

The higher-order tensor renormalization group is a tensor-network method providing estimates for the partition function and thermodynamical observables of classical and quantum systems in thermal equilibrium. At every step of the iterative…

High Energy Physics - Lattice · Physics 2023-02-22 Jacques Bloch , Robert Lohmayer , Maximilian Meister , Michael Nunhofer

Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the…

Signal Processing · Electrical Eng. & Systems 2020-02-19 Charilaos I. Kanatsoulis , Xiao Fu , Nicholas D. Sidiropoulos , Mehmet Akçakaya

We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization…

Statistics Theory · Mathematics 2020-01-29 Jing Lei

We characterize the fundamental limits of high-dimensional mean testing under arbitrary truncation, where samples are drawn from the conditional distribution $P(\cdot \mid S)$ for an unknown truncation set $S$ that may hide up to an…

Machine Learning · Statistics 2026-05-05 Yuhao Wang , Roberto Imbuzeiro Oliveira , Themis Gouleakis

We establish conditions for uniform $r$-th moment bound of certain $\R^d$-valued functions of a discrete-time stochastic process taking values in a general metric space. The conditions include an appropriate negative drift together with a…

Probability · Mathematics 2011-07-26 Arnab Ganguly , Debasish Chatterjee , John Lygeros , Heinz Koeppl

The error correcting capabilities of the Calderbank-Shor-Steane [[7,1,3]] quantum code, together with a fault-tolerant syndrome extraction by means of several ancilla states, have been numerically studied. A simple probability expression to…

Quantum Physics · Physics 2007-05-23 Pedro J. Salas

We discuss the possibility of performing precise tests of $\mu/e$ universality in $B \to\pi \ell^+\ell^-$ decays. We show that in wide regions of the dilepton invariant mass spectrum the ratio between muonic and electronic decay widths can…

High Energy Physics - Phenomenology · Physics 2021-10-13 Marzia Bordone , Claudia Cornella , Gino Isidori , Matthias König

We perform a systematic WKB expansion to all orders for a one-dimensional system with potential $V(x)=U_0/\cos^2{(\alpha x)}$. We are able to sum the series to the exact energy spectrum. Then we show that at any finite order the error of…

Quantum Physics · Physics 2016-09-08 Marko Robnik , Luca Salasnich

The performance of kernel density estimators is usually studied via Taylor expansions and asymptotic approximation arguments, in which the bandwidth parameter tends to zero with increasing sample size. In contrast, this paper focusses…

Statistics Theory · Mathematics 2026-02-25 Nils Lid Hjort , Nikolai G. Ushakov