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We derive the tail inequalities between two random variables starting from inequalities between its moment, or more generally between its Lebesgue-Riesz norms, which holds true on certain sets of parameters. We consider some applications…

Probability · Mathematics 2022-06-06 M. R. Formica , E. Ostrovsky , L. Sirota

H\"older estimates and Harnack inequalities are studied for fully nonlinear integro-differential equations under some mild assumptions. We allow the kernels of variable order and critically close to 2.

Analysis of PDEs · Mathematics 2022-07-07 Shuhei Kitano

We investigate concentration inequalities for Dirichlet and Multinomial random variables.

Machine Learning · Computer Science 2020-02-03 Jian Qian , Ronan Fruit , Matteo Pirotta , Alessandro Lazaric

Concentration inequalities are widely used for analyzing machine learning algorithms. However, current concentration inequalities cannot be applied to some of the most popular deep neural networks, notably in natural language processing.…

Machine Learning · Statistics 2021-03-22 Rémy Garnier , Raphaël Langhendries

We present novel martingale concentration inequalities for martingale differences with finite Orlicz-$\psi_\alpha$ norms. Such martingale differences with weak exponential-type tails scatters in many statistical applications and can be…

Probability · Mathematics 2020-03-19 Chris Junchi Li

We present a few techniques for proving $L^p$ estimates for martingales. Basic applications to It\^o integration and rough paths are included.

Probability · Mathematics 2024-04-29 Pavel Zorin-Kranich

In this note we extend kernel function approximation results for neural networks with Gaussian-distributed weights to single-layer networks initialized using Haar-distributed random orthogonal matrices (with possible rescaling). This is…

Machine Learning · Computer Science 2021-04-14 James Martens

In the multidimensional setting, we consider the errors-in-variables model. We aim at estimating the unknown nonparametric multivariate regression function with errors in the covariates. We devise an adaptive estimator based on projection…

Statistics Theory · Mathematics 2016-01-13 Michaël Chichignoud , Van Ha Hoang , Thanh Mai Pham Ngoc , Vincent Rivoirard

Nerman's martingale plays a central role in the law of large numbers for both, single- and multi-type, supercritical general branching processes. There are further, complex-valued Nerman-type martingales in the single-type process that…

Probability · Mathematics 2025-07-30 Konrad Kolesko , Matthias Meiners , Ivana Tomic

Variational interpolants are an indispensable tool for the construction of gradient-flow solutions via the Minimizing Movement Scheme. The De Giorgi lemma provides the associated discrete energy-dissipation inequality. It was originally…

Analysis of PDEs · Mathematics 2026-03-19 Alexander Mielke , Riccarda Rossi

The notion of margin loss has been central to the development and analysis of algorithms for binary classification. To date, however, there remains no consensus as to the analogue of the margin loss for multiclass classification. In this…

Machine Learning · Statistics 2024-05-20 Yutong Wang , Clayton Scott

We develop a martingale approximation approach to studying the limiting behavior of quadratic forms of Markov chains. We use the technique to examine the asymptotic behavior of lag-window estimators in time series and we apply the results…

Probability · Mathematics 2011-08-16 Yves F. Atchade , Matias D. Cattaneo

We study best approximations in Banach spaces via Birkhoff-James orthogonality of functionals. To exhibit the usefulness of Birkhoff-James orthogonality techniques in the study of best approximation problems, some algorithms and distance…

Functional Analysis · Mathematics 2021-04-30 Debmalya Sain , Saikat Roy

We establish a large deviation principle for the solutions of a class of stochastic partial differential equations with non-Lipschitz continuous coefficients. As an application, the large deviation principle is derived for super-Brownian…

Probability · Mathematics 2012-05-11 Parisa Fatheddin , Jie Xiong

We establish a central limit theorem and an invariance principle for stationary random fields, with projective-type conditions. Our result is obtained via an m-dependent approximation method. As applications, we establish invariance…

Probability · Mathematics 2012-04-12 Yizao Wang , Michael Woodroofe

In this paper non-asymptotic exponential estimates are derived for the tail distribution of polynomial martingale differences in terms unconditional tails distributions of summands. Applications are considered in the theory of polynomials…

Probability · Mathematics 2007-05-23 Eugene Ostrovsky

We construct a Banach rearrangement invariant norm on the measurable space for which the finiteness of this norm for measurable function (random variable) is equivalent to suitable tail (heavy tail and light tail) behavior. We investigate…

Functional Analysis · Mathematics 2012-10-04 E. Ostrovsky , L. Sirota

We prove new inequalities for the spectral radius, essential spectral radius, operator norm, measure of noncompactness and numerical radius of Hadamard weighted geometric means of positive kernel operators on Banach function and sequence…

Functional Analysis · Mathematics 2022-02-01 Katarina Bogdanović , Aljoša Peperko

In this paper non-asymptotic exponential and moment estimates are derived for tail of distribution for discrete time martingale and martingale transform by means of martingale differences in the terms of moments and tails of distributions…

Probability · Mathematics 2012-06-22 E. Ostrovsky , L. Sirota

Inspired by Berndtsson's work on the subharmonicity property of the Bergman kernel, we give a local variation formula of the full Bergman kernels associated to deformations of complex manifolds. In compact case, it follows from the…

Complex Variables · Mathematics 2013-07-23 Xu Wang