Julien Mairal


In this talk, we present simple ideas to combine nonparametric approaches based on positive definite kernels with deep learning models. There are many good reasons for bridging these two worlds. On the one hand, we want to provide regularization mechanisms and a geometric interpretation to deep learning models, as well as a functional space that allows to study their theoretical properties (eg invariance and stability). On the other hand, we want to bring more adaptivity and scalability to traditional kernel methods, which are crucially lacking. We will start this presentation by introducing models to represent graph data, then move to biological sequences, and images, showing that our hybrid models can achieves state-of-the-art results for many predictive tasks, especially when large amounts of annotated data are not available. This presentation is based on joint works with Alberto Bietti, Dexiong Chen, and Laurent Jacob.

Our speaker

Julien Mairal is a research scientist at Inria Grenoble, where he leads the Thoth research team. He joined Inria Grenoble in 2012, after a post-doc in the statistics department of UC Berkeley. He received the Ph.D. degree from Ecole Normale Superieure, Cachan. His research interests include machine learning, computer vision, mathematical optimization, and statistical image and signal processing. In 2016, he received a Starting Grant from the European Research Council. He was awarded the Cor Baayen prize in 2013, the IEEE PAMI young researcher award in 2017 and the test-of-time award at ICML 2019.