Rencontre d’avril 2025: Optimizing ROC curves using torch in R
Rencontres
torch
ROC
La prochaine rencontre de R-Toulouse est prévue le mercredi 9 avril 2025 de 17h00 à 18h00, dans l’auditorium du Centre de Biology Integrative de Toulouse (métro Université Paul Sabatier, ligne B).
La salle sera accessible à toutes et tous sur inscription, il faudra se présenter à l’accueil du bâtiment. Des pancartes R Toulouse seront idéalement placées pour vous guider.
** Attention** : pour des raisons de sécurité, l’inscription à ces rencontres est obligatoire pour pouvoir entrer au CBI: https://evento.renater.fr/survey/presence-a-r-toulouse-le-09-04-2025-5cnrg5p3
Programme
Optimizing ROC curves using torch in R, par Toby Dylan Hocking de l’université de Sherbrooke (CA).
Abstract. Receiver Operating Characteristic (ROC) curves are useful for evaluating binary classification models, especially when data are unbalanced (97% negative, 3% positive, as in medical diagnosis, image segmentation, etc). We propose a new surrogate loss function called the AUM, which can be used to optimize ROC curves during gradient descent learning. Whereas previous loss functions are based on summing over all labeled examples or pairs, the AUM requires a sort and a sum over the sequence of points on the ROC curve. We show how the AUM loss can be easily implemented in torch code (using R or python), so the ROC curve optimization objective can be used during neural network training (in addition to its typical use for evaluation). In our empirical study of unbalanced binary classification problems, we show that our new AUM minimization learning algorithm results in improved AUC and speed relative to previous baselines.
