3 papers
stat.ML2019
Metric Learning from Imbalanced Data
Léo Gautheron, Emilie Morvant, Amaury Habrard +1
A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points. Given a task, such a function can be optimized with a…
stat.ML2019
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard +3
We propose a Gradient Boosting algorithm for learning an ensemble of kernel functions adapted to the task at hand. Unlike state-of-the-art Multiple Kernel Learning techniques that…
cs.LG2018
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
Léo Gautheron, Ievgen Redko, Carole Lartizien
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical…