Portrait of Nicolas Le Roux

Nicolas Le Roux

Core Industry Member
lerouxni@mila.quebec
Canada CIFAR AI Chair
Adjunct Professor, McGill University, School of Computer Science
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Scientist, Microsoft Research

Biography

I am an academic researcher with expertise in machine learning, computer vision, neural networks, deep learning, optimization, large-scale learning and statistical modelling in general.

Current Students

Publications

Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold
Pierre-Antoine Manzagol
Reza Babanezhad Harikandeh
Ioannis Mitliagkas
Nicolas Le Roux
Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing pas… (see more)t gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting. One issue is the staleness due to using past gradients. We propose to correct this staleness using the idea of implicit gradient transport (IGT) which transforms gradients computed at previous iterates into gradients evaluated at the current iterate without using the Hessian explicitly. In addition to reducing the variance and bias of our updates over time, IGT can be used as a drop-in replacement for the gradient estimate in a number of well-understood methods such as heavy ball or Adam. We show experimentally that it achieves state-of-the-art results on a wide range of architectures and benchmarks. Additionally, the IGT gradient estimator yields the optimal asymptotic convergence rate for online stochastic optimization in the restricted setting where the Hessians of all component functions are equal.
arxiv.org
Understanding the impact of entropy in policy learning
Zafarali Ahmed
Nicolas Le Roux
Mohammad Norouzi
Dale Schuurmans
Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with \emph{explorat… (see more)ion} by encouraging the selection of more stochastic policies. In this work, we analyze this claim using new visualizations of the optimization landscape based on randomly perturbing the loss function. We first show that even with access to the exact gradient, policy optimization is difficult due to the geometry of the objective function. Then, we qualitatively show that in some environments, a policy with higher entropy can make the optimization landscape smoother, thereby connecting local optima and enabling the use of larger learning rates. This paper presents new tools for understanding the optimization landscape, shows that policy entropy serves as a regularizer, and highlights the challenge of designing general-purpose policy optimization algorithms.
2018-11-27
(published)
www.semanticscholar.org
Combining adaptive algorithms and hypergradient method: a performance and robustness study
Akram Erraqabi
Nicolas Le Roux
2018-09-27
(published)
www.semanticscholar.org
Online Hyper-Parameter Optimization
Damien Vincent
Sylvain Gelly
Nicolas Le Roux
Olivier Bousquet
2018-02-15
(published)
www.semanticscholar.org
Online variance-reducing optimization
Nicolas Le Roux
Reza Babanezhad 0001
Reza Babanezhad Harikandeh
Pierre-Antoine Manzagol
2018-02-12
International Conference on Learning Representations (published)
dblp.uni-trier.de
Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain
Nicolas Le Roux
Pierre-Antoine Manzagol
The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research… (see more). In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalues are and the benefits one can observe in handling them appropriately.
2018-01-01
ICLR (Workshop) (published)
arxiv.org
arxiv.org
BOUNDS LEAD TO IMPROVED CLASSIFIERS
Nicolas Le Roux
The standard approach to supervised classification involves the minimization of a log-loss as an upper bound to the classification error. Wh… (see more)ile this is a tight bound early on in the optimization, it overemphasizes the influence of incorrectly classified examples far from the decision boundary. Updating the upper bound during the optimization leads to improved classification rates while transforming the learning into a sequence of minimization problems. In addition, in the context where the classifier is part of a larger system, this modification makes it possible to link the performance of the classifier to that of the whole system, allowing the seamless introduction of external constraints.
2017-01-01
(published)
www.semanticscholar.org