Welcome
I am a researcher with expertise in machine learning, neural networks, optimization, large-scale learning and statistical modeling in general. I am now at Microsoft Research in Montreal and an adjunct faculty at both McGill and UdeM.
I am a co-recipient of the 2018 Lagrange prize with Francis Bach and Mark Schmidt.
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 (in review)
- 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: I am looking for a postdoc with relevant prior expertise to work on the topic. If you are interested, send me a message with links and an explanation to your peer-reviewed papers on the topic.
- Download my resume (updated on 2021/1)
- 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.
You can find my publications on my Google Scholar page.