Publications related to A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Distributional Regression and Autoregression via Optimal Transport

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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EPFL2023

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

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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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We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...

OXFORD UNIV PRESS2022

Minimax rate for optimal transport regression between distributions

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Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

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Fast screening of covariates in population models empowered by machine learning

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One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...

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Elsevier2021

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Background Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithm ...

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Background: Understanding the association between motor capacity (MC) (what people can do in a standardized environment), mobility performance (MP) (what people actually do in real-life) and falls is important for early detection of and counteracting on fu ...

ELSEVIER IRELAND LTD2020

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Jan Sickmann Hesthaven, Mariella Kast, Mengwu Guo

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

ELSEVIER SCIENCE SA2020

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

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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

Minimax rate for optimal transport regression between distributions

Fast screening of covariates in population models empowered by machine learning

Sparsest piecewise-linear regression of one-dimensional data

Swimming Phase-Based Performance Evaluation Using a Single IMU in Main Swimming Techniques

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 non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

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

Fast screening of covariates in population models empowered by machine learning

Minimax rate for optimal transport regression between distributions

Swimming Phase-Based Performance Evaluation Using a Single IMU in Main Swimming Techniques

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 non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Sparsest piecewise-linear regression of one-dimensional data