Search Results for author: Johan Segers

Found 6 papers, 0 papers with code

Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates

no code implementations2 Feb 2024 Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman

Spherical harmonics are polynomials on the sphere that form an orthonormal basis of the set of square-integrable functions on the sphere.

A Quadrature Rule combining Control Variates and Adaptive Importance Sampling

no code implementations24 May 2022 Rémi Leluc, François Portier, Johan Segers, Aigerim Zhuman

Within the standard adaptive importance sampling framework, a simple weighted least squares approach is proposed to improve the procedure with control variates.

Concentration bounds for the empirical angular measure with statistical learning applications

no code implementations7 Apr 2021 Stéphan Clémençon, Hamid Jalalzai, Stéphane Lhaut, Anne Sabourin, Johan Segers

The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins.

Binary Classification Unsupervised Anomaly Detection +1

Risk bounds when learning infinitely many response functions by ordinary linear regression

no code implementations16 Jun 2020 Vincent Plassier, François Portier, Johan Segers

Consider the problem of learning a large number of response functions simultaneously based on the same input variables.

regression

Control variate selection for Monte Carlo integration

no code implementations26 Jun 2019 Rémi Leluc, François Portier, Johan Segers

Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a multiple linear regression model with the integrand as response and the control variates as covariates.

Statistics Theory Statistics Theory

Bayesian inference for bivariate ranks

no code implementations9 Feb 2018 Simon Guillotte, François Perron, Johan Segers

A recommender system based on ranks is proposed, where an expert's ranking of a set of objects and a user's ranking of a subset of those objects are combined to make a prediction of the user's ranking of all objects.

Bayesian Inference Recommendation Systems

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