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Related papers: Wick Calculus For Nonlinear Gaussian Functionals

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We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include…

Machine Learning · Statistics 2022-10-25 Victor Chernozhukov , Whitney Newey , Rahul Singh

We propose a formulation for nonlinear recurrent models that includes simple parametric models of recurrent neural networks as a special case. The proposed formulation leads to a natural estimator in the form of a convex program. We provide…

Machine Learning · Statistics 2019-08-28 Sohail Bahmani , Justin Romberg

A mild formulation for stochastic parabolic Anderson model with time-homogeneous Gaussian potential suggests a way of defining a solution to obtain its optimal regularity. Two different interpretations in the equation or in the mild…

Analysis of PDEs · Mathematics 2018-09-18 Hyun-Jung Kim

We introduce the notion of {\em covariance measure structure} for square integrable stochastic processes. We define Wiener integral, we develop a suitable formalism for stochastic calculus of variations and we make Gaussian assumptions only…

Probability · Mathematics 2007-05-23 Ida Kruk , Francesco Russo , Ciprian Tudor

This paper is concerned with linear stochastic systems whose output is a stationary Gaussian random process related by an integral operator to a standard Wiener process at the input. We consider a performance criterion which involves the…

Optimization and Control · Mathematics 2022-04-28 Igor G. Vladimirov , Ian R. Petersen

Perturbative and non-perturbative results are presented on the renormalization constants of the quark field and the vector, axial-vector, pseudoscalar, scalar and tensor currents. The perturbative computation, carried out at one-loop level…

High Energy Physics - Lattice · Physics 2013-05-30 C. Alexandrou , M. Constantinou , T. Korzec , H. Panagopoulos , F. Stylianou

The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity, and in the development of quantum technologies. Efficient techniques such as those based on the Gottesman-Knill theorem for…

Quantum Physics · Physics 2025-09-30 Matthew L. Goh , Martin Larocca , Lukasz Cincio , M. Cerezo , Frédéric Sauvage

We introduce a variational theory for processes adapted to the multi-dimensional Brownian motion filtration. The theory provides a differential structure which describes the infinitesimal evolution of Wiener functionals at very small…

Probability · Mathematics 2017-07-13 Alberto Ohashi , Dorival Leão , Alexandre B. Simas

We prove a local limit theorem, i.e. a central limit theorem for densities, for a sequence of independent and identically distributed random variables taking values on an abstract Wiener space; the common law of those random variables is…

Probability · Mathematics 2016-10-05 Alberto Lanconelli , Aurel Iulian Stan

A model of a system driven by quantum white noise with singular quadratic self--interaction is considered and an exact solution for the evolution operator is found. It is shown that the renormalized square of the squeezed classical white…

Quantum Physics · Physics 2007-05-23 L. Accardi , I. V. Volovich

Reconstructing noise-driven nonlinear networks from time series of output variables is a challenging problem, which turns to be very difficult when nonlinearity of dynamics, strong noise impacts and low measurement frequencies jointly…

Statistical Mechanics · Physics 2017-10-20 Rundong Shi , Gang Hu , Shihong Wang

Neurons in the brain show great diversity in their individual properties and their connections to other neurons. To develop an understanding of how neuronal diversity contributes to brain dynamics and function at large scales we start with…

Disordered Systems and Neural Networks · Physics 2025-07-21 Jie Zang , Pascal Helson , Shenquan Liu , Arvind Kumar , Dhrubaditya Mitra

An $\hbar$-expansion is presented for the ensemble-averaged spectral function of noninteracting matter waves in random potentials. We obtain the leading quantum corrections to the deep classical limit at high energies by the Wigner-Weyl…

Quantum Gases · Physics 2015-06-01 Martin-Isbjörn Trappe , Dominique Delande , Cord A. Müller

We establish the strong L2(P)-convergence of properly rescaled Wick powers as the power index tends to infinity. The explicit representation of such limit will also provide the convergence in distribution to normal and log-normal random…

Probability · Mathematics 2008-09-23 Alberto Lanconelli

Motivated by the hyper-holomorphic case we introduce and study rational functions in the setting of Hida's white noise space. The Fueter polynomials are replaced by a basis computed in terms of the Hermite functions, and the…

Probability · Mathematics 2008-02-19 Daniel Alpay , David Levanony

We give a definition of admissible counterterms appropriate for massive quantum field theories on the noncommutative Minkowski space, based on a suitable notion of locality. We then define products of fields of arbitrary order, the…

High Energy Physics - Theory · Physics 2009-01-27 D. Bahns , S. Doplicher , K. Fredenhagen , G. Piacitelli

We provide a general result for finding Stein operators for the product of two independent random variables whose Stein operators satisfy a certain assumption, extending a recent result of Gaunt, Mijoule and Swan \cite{gms18}. This…

Probability · Mathematics 2020-09-28 Robert E. Gaunt , Guillaume Mijoule , Yvik Swan

We combine Malliavin calculus with Stein's method, in order to derive explicit bounds in the Gaussian and Gamma approximations of random variables in a fixed Wiener chaos of a general Gaussian process. We also prove results concerning…

Probability · Mathematics 2008-05-10 Ivan Nourdin , Giovanni Peccati

In this paper, we consider the Gibbs measures associated with Euclidean quantum field theory with polynomial-type of interactions on the torus. We observe the (non-)normalizability of the multivariate version of $P(\Phi)_2$ models by the…

Probability · Mathematics 2024-12-16 Hirotatsu Nagoji

Reversible Instance Normalization (RevIN) is a key technique enabling simple linear models to achieve state-of-the-art performance in time series forecasting. While replacing its non-robust statistics with robust counterparts (termed…

Machine Learning · Computer Science 2025-10-07 Fanzhe Fu , Yang Yang
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