Quentin BERTRAND - Home

I am a post-doctoral researcher at Mila working with Gauthier Gidel and Simon Lacoste-Julien. On July 1st, 2024 I will join Inria as a Research Scientist!
I work on automatic differentiation, games, and representation learning.

Prior to this position, I did my Ph. D. at Inria Paris-Saclay (in the Parietal Team) under the supervision of Joseph Salmon and Alexandre Gramfort.
I worked on the optimization and statistical aspects of high dimensional sparse linear regression applied to brain signal reconstruction.
In particular, I developed Python packages for fast computation and automatic hyperparameter selection of sparse linear models.

Here is a short resume and my list of publications.

Contact

Email: quentin [dot] bertrand AT mila [dot] quebec

News

01-16-2024 Our paper On the Stability of Iterative Retraining of Generative Models on their own Data was accepted to ICLR 2024 with a spotlight, see you in Vienna!
12-18-2023 We just released our paper proving that Q-learners can provably learn to collude in the iterated prisoner dilemma!
01-07-2023 On July 1st, 2024 I will join Inria as a Research Scientist!

Previous News

05-12-2023 I will present our paper On the Limitations of Elo: Real-World Games are Transitive, not Additive at the Berkeley Multi-Agent Reinforcement Learning Seminar
Our paper Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning has been accepted at ICML 2023, see you in Hawaii!
Our paper On the Limitations of Elo: Real-World Games are Transitive, not Additive has been accepted to AISTATS 2023, see you in Spain!
I just presented our paper Synergies between Disentanglement and Sparsity: a Multi-task Learning Perspective at the Canadian Mathematical Society Winter Workshop
I just presented our two papers Beyond L1: Faster and Better Sparse Models with skglm and The Curse of Unrolling: Rate of Differentiating Through Optimization at NeurIPS 2022
I was awarded the top reviewer award at NeurIPS 2022!
Our papers Beyond L1: Faster and Better Sparse Models with skglm and The Curse of Unrolling: Rate of Differentiating Through Optimization have been accepted to NeurIPS 2022!