3 citations · 7 across the 5 of their papers we have counts for
5 papers
Sharp error bounds for imbalanced classification: how many examples in the minority class?
Anass Aghbalou, François Portier, Anne Sabourin
When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates wi…
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
Anass Aghbalou, Guillaume Staerman
Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to t…
On the bias of K-fold cross validation with stable learners
Anass Aghbalou, François Portier, Anne Sabourin
This paper investigates the efficiency of the K-fold cross-validation (CV) procedure and a debiased version thereof as a means of estimating the generalization risk of a learning a…
Cross-validation on Extreme Regions
Anass Aghbalou, Patrice Bertail, François Portier +1
We conduct a non asymptotic study of the Cross Validation (CV) estimate of the generalization risk for learning algorithms dedicated to extreme regions of the covariates space. In…
Tail inverse regression for dimension reduction with extreme response
Anass Aghbalou, François Portier, Anne Sabourin +1
We consider the problem of supervised dimension reduction with a particular focus on extreme values of the target to be explained by a covariate vector $X \in \mat…