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2020 – today
- 2023
[b1]Hugo Schmutz:
Semi-supervised learning, PET/CT segmentation and prediction of the tumoral response to immunotherapy. (Apprentissage semi-supervisé, segmentation d'images TEP/TDM et prédiction de la réponse tumorale à l'immunothérapie). Côte d'Azur University, Nice, France, 2023- [c3]Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei:
Don't fear the unlabelled: safe semi-supervised learning via debiasing. ICLR 2023 - [c2]Aude Sportisse, Hugo Schmutz, Olivier Humbert, Charles Bouveyron, Pierre-Alexandre Mattei:
Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. ICML 2023: 32521-32539 - 2022
- [c1]Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen:
Model-agnostic out-of-distribution detection using combined statistical tests. AISTATS 2022: 10753-10776 - [i2]Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen:
Model-agnostic out-of-distribution detection using combined statistical tests. CoRR abs/2203.01097 (2022) - [i1]Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei:
Don't fear the unlabelled: safe deep semi-supervised learning via simple debiasing. CoRR abs/2203.07512 (2022)
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