12 citations · 45 across the 20 of their papers we have counts for
20 papers
End-to-end Early Classification of Time Series in Non-Stationary Environments
Aurélien Renault, Alexis Bondu, Antoine Cornuéjols +1
Early Classification of Time Series (ECTS) requires making accurate decisions as early as possible in inherently online and evolving environments. Yet, most existing methods assume…
Early Classification of Time Series in Non-Stationary Cost Regimes
Aurélien Renault, Alexis Bondu, Antoine Cornuéjols +1
Early Classification of Time Series (ECTS) addresses decision-making problems in which predictions must be made as early as possible while maintaining high accuracy. Most existing…
Khiops: An End-to-End, Frugal AutoML and XAI Machine Learning Solution for Large, Multi-Table Databases
Marc Boullé, Nicolas Voisine, Bruno Guerraz +10
Khiops is an open source machine learning tool designed for mining large multi-table databases. Khiops is based on a unique Bayesian approach that has attracted academic interest w…
Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL
Aurélien Renault, Alexis Bondu, Antoine Cornuéjols +1
Early Classification of Time Series (ECTS) is vital in fields like industrial monitoring and medical triage, where quick and accurate predictions are essential. One of the core cha…
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
Thomas George, Pierre Nodet, Alexis Bondu +1
Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection…
ml_edm package: a Python toolkit for Machine Learning based Early Decision Making
Aurélien Renault, Youssef Achenchabe, Édouard Bertrand +4
\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing research…
Early Classification of Time Series: A Survey and Benchmark
Aurélien Renault, Alexis Bondu, Antoine Cornuéjols +1
In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too…
Early Detection of Critical Urban Events using Mobile Phone Network Data
Pierre Lemaire, Angelo Furno, Stefania Rubrichi +5
Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by indivi…
Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts
Pierre Nodet, Vincent Lemaire, Alexis Bondu +1
Training machine learning models from data with weak supervision and dataset shifts is still challenging. Designing algorithms when these two situations arise has not been explored…
biquality-learn: a Python library for Biquality Learning
Pierre Nodet, Vincent Lemaire, Alexis Bondu +1
The democratization of Data Mining has been widely successful thanks in part to powerful and easy-to-use Machine Learning libraries. These libraries have been particularly tailored…
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
Aurélien Renault, Alexis Bondu, Vincent Lemaire +1
Time Series Classification (TSC) has received much attention in the past two decades and is still a crucial and challenging problem in data science and knowledge engineering. Indee…
Open challenges for Machine Learning based Early Decision-Making research
Alexis Bondu, Youssef Achenchabe, Albert Bifet +6
More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends…
When to Classify Events in Open Times Series?
Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols +1
In numerous applications, for instance in predictive maintenance, there is a pression to predict events ahead of time with as much accuracy as possible while not delaying the decis…
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…
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…
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…
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…
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…
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…
Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms
Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols +1
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction. In…