3 papers
cs.LG2026
PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging
Théo Gnassounou, Antoine Collas, Rémi Flamary +1
Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and reco…
cs.LG2025
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
Yanis Lalou, Théo Gnassounou, Antoine Collas +6
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.…
stat.ML2024
Geodesic Optimization for Predictive Shift Adaptation on EEG data
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the d…