12 citations · 67 across the 26 of their papers we have counts for
9 papers · 1 filter
A Practical Approach to Novel Class Discovery in Tabular Data
Colin Troisemaine, Alexandre Reiffers-Masson, Stéphane Gosselin +2
The problem of Novel Class Discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled set of novel classes. While NCD…
Evidential uncertainty sampling for active learning
Arthur Hoarau, Vincent Lemaire, Arnaud Martin +2
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In thi…
Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers
Vincent Lemaire, Nathan Le Boudec, Victor Guyomard +1
There are now many explainable AI methods for understanding the decisions of a machine learning model. Among these are those based on counterfactual reasoning, which involve simula…
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…