Publications associées à On distributional autoregression and iterated transportation

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

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Amer Chemical Soc2024

Iterative removal of sources to model the turbulent electromotive force

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Oxford Univ Press2024

Random matrix methods for high-dimensional machine learning models

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EPFL2024

Sparse autoregressive neural networks for classical spin systems

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

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Distributional Regression and Autoregression via Optimal Transport

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We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

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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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Comparison of Three Imputation Methods for Groundwater Level Timeseries

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This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

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Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

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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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