54 citations · 180 across the 26 of their papers we have counts for
5 papers · 1 filter
A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning
Joon Kwon, Guillaume Lecué, Matthieu Lerasle
Hyperparameters tuning and model selection are important steps in machine learning. Unfortunately, classical hyperparameter calibration and model selection procedures are sensitive…
A Bayesian nonparametric approach for generalized Bradley-Terry models in random environment
Sylvain Le Corff, Matthieu Lerasle, Elodie Vernet
This paper deals with the estimation of the unknown distribution of hidden random variables from the observation of pairwise comparisons between these variables. This problem is in…
Robust classification via MOM minimization
Guillaume Lecué, Matthieu Lerasle, Timothée Mathieu
We present an extension of Vapnik's classical empirical risk minimizer (ERM) where the empirical risk is replaced by a median-of-means (MOM) estimator, the new estimators are calle…
Density estimation for RWRE
Antoine Havet, Matthieu Lerasle, Éric Moulines
We consider the problem of non-parametric density estimation of a random environment from the observation of a single trajectory of a random walk in this environment. We first cons…
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
Matthieu Lerasle, Zoltan Szabo, Timothee Mathieu +1
Mean embeddings provide an extremely flexible and powerful tool in machine learning and statistics to represent probability distributions and define a semi-metric (MMD, maximum mea…