FLAMARY , Remi

We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation. The algorithm consists in obtaining a descent...

Kernel methods have proven to be useful and successful to analyse large-scale multi-omics datasets [Schölkopf et al., 2004]. However, as stated in [Hofmann et al., 2015, Mariette et al., 2017], these methods usually suffer from a lack of interpretability as the information of thousands descriptors is summarized in a few similarity measures,...

International audience

In this work, we propose a novel linear classification scheme for non-stationary periodic data. We express the classifier in a temporal basis while regularizing its temporal complexity leading to a convex optimization problem. Numerical experiments show very good results on a simulated example and on real life remote sensing image...

In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform...