Louis Lacombe
As an econometrician at the Erasmus School of Economics and later a data scientist at Bocconi, my journey in uncertainty quantification began during my internship at Quantmetry, where I contributed to the development of MAPIE. I implemented conformalized quantile regression and became a core developer of the library, collaborating closely with Thibault Cordier, Vincent Blot, and Candice Moyet.
Currently, at Capgemini Invent's R&I lab, we continue to advance MAPIE while also exploring cutting-edge topics such as hallucinations in large language models (LLMs) and combining knowledge graphs with LLMs.
Sessions
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