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Fast Gaussian Process Regression Using KD-Trees

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Distributional Regression and Autoregression via Optimal Transport

Laya Ghodrati

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

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

EPFL2023

Fast Gaussian Process Regression Using KD-Trees

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Chat with Graph Search

Distributional Regression and Autoregression via Optimal Transport

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Distribution-on-distribution regression via optimal transport maps

Gaussian Process Regression for Materials and Molecules

Physics-Inspired Structural Representations for Molecules and Materials

Sparsest piecewise-linear regression of one-dimensional data

A memory of motion for visual predictive control tasks

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

The association of basic and challenging motor capacity with mobility performance and falls in young seniors

Distribution-on-distribution regression via optimal transport maps

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Distributional Regression and Autoregression via Optimal Transport

Physics-Inspired Structural Representations for Molecules and Materials

Sparsest piecewise-linear regression of one-dimensional data

Gaussian Process Regression for Materials and Molecules

A memory of motion for visual predictive control tasks

The association of basic and challenging motor capacity with mobility performance and falls in young seniors

Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms