27 citations · 54 across the 18 of their papers we have counts for
7 papers · 1 filter
Riemannian Stochastic Optimization for Sufficient Dimension Reduction
Thibault Pautrel, François Portier
Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of…
Concentration and excess risk bounds for imbalanced classification with synthetic oversampling
Touqeer Ahmad, Mohammadreza M. Kalan, François Portier +1
Synthetic oversampling of minority examples using SMOTE and its variants is a leading strategy for addressing imbalanced classification problems. Despite the success of this approa…
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates
Rémi Leluc, Aymeric Dieuleveut, François Portier +2
The Sliced-Wasserstein (SW) distance between probability measures is defined as the average of the Wasserstein distances resulting for the associated one-dimensional projections. A…
Nearest Neighbor Sampling for Covariate Shift Adaptation
François Portier, Lionel Truquet, Ikko Yamane
Many existing covariate shift adaptation methods estimate sample weights given to loss values to mitigate the gap between the source and the target distribution. However, estimatin…
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Rémi Leluc, François Portier, Johan Segers +1
Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration.…
SGD with Coordinate Sampling: Theory and Practice
Rémi Leluc, François Portier
While classical forms of stochastic gradient descent algorithm treat the different coordinates in the same way, a framework allowing for adaptive (non uniform) coordinate sampling…
Risk bounds when learning infinitely many response functions by ordinary linear regression
Vincent Plassier, François Portier, Johan Segers
Consider the problem of learning a large number of response functions simultaneously based on the same input variables. The training data consist of a single independent random sam…