2 citations · 4 across the 3 of their papers we have counts for
3 papers
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Aude Sportisse, Hugo Schmutz, Olivier Humbert +2
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels,…
Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn +5
We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sens…
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei
Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount o…