Gaspard Beugnot

2nd year PhD student in Machine Learning and Optimization.


Welcome to my personal page. Learn more about my research interests and my latest news here!

I’m currently a second-year PhD student at ENS and Inria, under the supervision of Julien Mairal and Alessandro Rudi. My research focuses on theoretical properties of kernel methods in all sorts of flavour, but I’m also interested in providing fast and reliable implementation of my research projects.

Currently, I’m interested in non-convex optimization, and I’m enthusiastic about Rust and Julia, which I hope to use for my research along with Python!

Before that, I graduated from Ecole Polytechnique (X2016) and got a master from École Normale Supérieure in Mathematics, Vision and Machine Learning in 2020 (Master MVA).


May 20, 2022 Our paper on the influence of the learning on the generalization was accepted at COLT22! See you there!
Apr 15, 2022 The learning rate impacts the generalization when training neural network. Did you know it can occur in convex settings too? Check our last preprint!
Sep 28, 2021 My paper on the proximal point method for concordant loss function was awarded a Spotlight award at NeurIPS 2021!

selected publications

  1. NeurIPS21 Spotlight
    Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization
    Beugnot, GaspardMairal, Julien, and Rudi, Alessandro
    In Advances in Neural Information Processing Systems 2021
  2. COLT22
    On the Benefits of Large Learning Rates for Kernel Methods
    Beugnot, GaspardMairal, Julien, and Rudi, Alessandro