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Distribution-on-distribution regression via optimal transport maps

Publications associées (30)

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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Regressive Tomography and Direct Regressive Reconstruction: A Research Note

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Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

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Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

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Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...

ELSEVIER2023

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Yanina Yurina Shkel

Given two jointly distributed random variables (X,Y), a functional representation of X is a random variable Z independent of Y, and a deterministic function g(⋅,⋅) such that X=g(Y,Z). The problem of finding a minimum entropy functional representation is kn ...

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Acute TNF alpha levels predict cognitive impairment 6-9 months after COVID-19 infection

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PERGAMON-ELSEVIER SCIENCE LTD2023

Regularization for distributionally robust state estimation and prediction

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The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

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An equilibrated flux a posteriori error estimator for defeaturing problems

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An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

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Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

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The Institute of Electrical and Electronics Engineers, Inc2023

Stable parameterization of continuous and piecewise-linear functions

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Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

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Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

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In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

Springer Cham2024