collaborators

7 papers

cs.AI2021

Early and Revocable Time Series Classification

Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols +1

Many approaches have been proposed for early classification of time series in light of itssignificance in a wide range of applications including healthcare, transportation and fi-n…

cs.LG2021

Contrastive Representations for Label Noise Require Fine-Tuning

Pierre Nodet, Vincent Lemaire, Alexis Bondu +1

In this paper we show that the combination of a Contrastive representation with a label noise-robust classification head requires fine-tuning the representation in order to achieve…

cs.LG2021

Early Classification of Time Series is Meaningful

Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols +1

Many approaches have been proposed for early classification of time series in light of its significance in a wide range of applications including healthcare, transportation and fin…

cs.LG2021

Interpretable Feature Construction for Time Series Extrinsic Regression

Dominique Gay, Alexis Bondu, Vincent Lemaire +1

Supervised learning of time series data has been extensively studied for the case of a categorical target variable. In some application domains, e.g., energy, environment and healt…

cs.LG2020

From Weakly Supervised Learning to Biquality Learning: an Introduction

Pierre Nodet, Vincent Lemaire, Alexis Bondu +2

The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies". In WSL use cas…

cs.LG2020

Importance Reweighting for Biquality Learning

Pierre Nodet, Vincent Lemaire, Alexis Bondu +1

The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies", namely: poor q…