Search Results for author: Fabrice Gamboa

Found 10 papers, 4 papers with code

Conformal inference for regression on Riemannian Manifolds

no code implementations12 Oct 2023 Alejandro Cholaquidis, Fabrice Gamboa, Leonardo Moreno

Regression on manifolds, and, more broadly, statistics on manifolds, has garnered significant importance in recent years due to the vast number of applications for this type of data.

regression

Hoeffding decomposition of black-box models with dependent inputs

no code implementations10 Oct 2023 Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, Jean-Michel Loubes

The elements of this decomposition can be expressed using oblique projections and allow for novel interpretability indices for evaluation and variance decomposition purposes.

Uncertainty Quantification

Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models

1 code implementation23 Sep 2022 Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, Jean-Michel Loubes

Numerical experiments on real case studies, from the UQ and ML fields, highlight the computational feasibility of such studies and provide local and global insights on the robustness of black-box models to input perturbations.

Uncertainty Quantification

Dual optimal design and the Christoffel-Darboux polynomial

no code implementations8 Sep 2020 Yohann de Castro, Fabrice Gamboa, Didier Henrion, Jean Lasserre

The purpose of this short note is to show that the Christoffel-Darboux polynomial, useful in approximation theory and data science, arises naturally when deriving the dual to the problem of semi-algebraic D-optimal experimental design in statistics.

Optimization and Control Statistics Theory Statistics Theory

Explaining Machine Learning Models using Entropic Variable Projection

2 code implementations18 Oct 2018 François Bachoc, Fabrice Gamboa, Max Halford, Jean-Michel Loubes, Laurent Risser

In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections.

BIG-bench Machine Learning

Obtaining fairness using optimal transport theory

1 code implementation8 Jun 2018 Eustasio del Barrio, Fabrice Gamboa, Paula Gordaliza, Jean-Michel Loubes

\textit{Fairness} is generally studied in a probabilistic framework where it is assumed that there exists a protected variable, whose use as an input of the algorithm may imply discrimination.

Statistics Theory Statistics Theory 62H30, 68T01

Gaussian Processes indexed on the symmetric group: prediction and learning

no code implementations16 Mar 2018 François Bachoc, Baptiste Broto, Fabrice Gamboa, Jean-Michel Loubes

In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X.

Gaussian Processes

Deep Learning applied to Road Traffic Speed forecasting

no code implementations2 Oct 2017 Thomas Epelbaum, Fabrice Gamboa, Jean-Michel Loubes, Jessica Martin

In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression model for time dependent data.

regression

Approximate Optimal Designs for Multivariate Polynomial Regression

1 code implementation9 Jun 2017 Yohann De Castro, Fabrice Gamboa, Didier Henrion, Roxana Hess, Jean-Bernard Lasserre

We introduce a new approach aiming at computing approximate optimal designs for multivariate polynomial regressions on compact (semi-algebraic) design spaces.

Statistics Theory Information Theory Information Theory Numerical Analysis Computation Methodology Statistics Theory 62K05, 90C25 (Primary) 41A10, 49M29, 90C90, 15A15 (secondary)

A Gaussian Process Regression Model for Distribution Inputs

no code implementations31 Jan 2017 François Bachoc, Fabrice Gamboa, Jean-Michel Loubes, Nil Venet

We prove that the Gaussian processes indexed by distributions corresponding to these kernels can be efficiently forecast, opening new perspectives in Gaussian process modeling.

BIG-bench Machine Learning Gaussian Processes +1

Cannot find the paper you are looking for? You can Submit a new open access paper.