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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

Distributional Regression and Autoregression via Optimal Transport

Distribution-on-distribution regression via optimal transport maps

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

Gaussian Process Regression for Materials and Molecules

Sparsest piecewise-linear regression of one-dimensional data

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

A memory of motion for visual predictive control tasks

Physics-Inspired Structural Representations for Molecules and Materials

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