Thibault Cordier
Thibault Cordier is a Data and Research Scientist at Capgemini Invent, where he is a member of the Lab Invent team in France and serves as the technical leader of the MAPIE project.
Prior to joining the research team at Capgemini Invent, he earned his PhD in Computer Science in 2023 at Avignon University.
Up to now, his research has focused on distribution-free inference and conformal prediction, with applications in computer vision, natural language processing, and time series analysis.
Session
MAPIE (Model Agnostic Prediction Interval Estimator) is your go-to solution for managing uncertainties and risks in machine learning models. This Python library, nestled within scikit-learn-contrib, offers a way to calculate prediction intervals with controlled coverage rates for regression, classification, and even time series analysis. But it doesn't stop there - MAPIE can also be used to handle more complex tasks like multi-label classification and semantic segmentation in computer vision, ensuring probabilistic guarantees on crucial metrics like recall and precision. MAPIE can be integrated with any model - whether it's scikit-learn, TensorFlow, or PyTorch. Join us as we delve into the world of conformal predictions and how to quickly manage your uncertainties using MAPIE.
Link to Github: https://github.com/scikit-learn-contrib/MAPIE