Welcome
I am a researcher with expertise in machine learning, neural networks, optimization, large-scale learning and statistical modeling in general. I am taking a break at the moment, though I am stil an adjunct faculty at both McGill and UdeM.
I am a co-recipient of the 2018 Lagrange prize with Francis Bach and Mark Schmidt, as well as a recipient of a CIFAR AI chair.
My two main interests at the moment are:
-
Understanding optimization in supervised learning and reinforcement
learning from the perspective of optimizing functions (or policies)
themselves rather than parameters. Here are papers I co-authored on the
topic:
- Tighter bounds lead to improved classifiers (ICLR 2017)
- An operator view of policy gradient methods (NeurIPS 2020)
- A general class of surrogate functions for stable and efficient reinforcement learning (AISTATS 2022 best paper runner-up)
- Target-based Surrogates for Stochastic Optimization (ICML 2023)
- Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees (NeurIPS 2023)
- Model adaptation and decentralization of power. How can we create simpler, modular architectures, that can be adapted and reused for other purposes, giving more control to the rightholders (thanks Shakir Mohamed for teaching me that term)? Technical papers I coauthored in that space are:
- Download my resume (updated on 2023/10)
- Email: nicolas@le-roux.name
Besides purely technical topics, I am also interested in team management and organization, and in particular how to effectively address stress, ensure well-being and achieve a truly inclusive environment in research.
I am not looking for any student at the moment.
You can find my publications on my Google Scholar page.