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cs.LG2026
Low-Budget Active Learning through Entropic Optimal Transport
Rim Hajal, Mathieu Besançon, Jérôme Malick
We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high accuracy on the selection only…
cs.LG2024
: a library for Wasserstein distributionally robust machine learning
Florian Vincent, Waïss Azizian, Franck Iutzeler +1
We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using Wasserstein distances, popular i…