63 citations · 98 across the 13 of their papers we have counts for
8 papers · 1 filter
LCE: An Augmented Combination of Bagging and Boosting in Python
Kevin Fauvel, Élisa Fromont, Véronique Masson +2
lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LC…
UniRank: Unimodal Bandit Algorithm for Online Ranking
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration,…
Discovering Useful Compact Sets of Sequential Rules in a Long Sequence
Erwan Bourrand, Luis Galárraga, Esther Galbrun +2
We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful se…
Position-Based Multiple-Play Bandits with Thompson Sampling
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
Multiple-play bandits aim at displaying relevant items at relevant positions on a web page. We introduce a new bandit-based algorithm, PB-MHB, for online recommender systems which…
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
Kevin Fauvel, Véronique Masson, Élisa Fromont
Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods. The framework details a set of characteristics…
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
Kevin Fauvel, Élisa Fromont, Véronique Masson +2
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-…