Nicolas Le Roux

According to our database, Nicolas Le Roux authored at least 63 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Language-guided Skill Learning with Temporal Variational Inference.

Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin, George Konidaris, Nicolas Le Roux, Marc-Alexandre Côté, Xingdi Yuan

CoRR, 2024

2023

Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference.

Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, Nicolas Le Roux

CoRR, 2023

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees.

Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Joint Prompt Optimization of Stacked LLMs using Variational Inference.

Alessandro Sordoni, Eric Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-Head Adapter Routing for Cross-Task Generalization.

Lucas Page-Caccia, Edoardo Maria Ponti, Zhan Su, Matheus Pereira, Nicolas Le Roux, Alessandro Sordoni

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Target-based Surrogates for Stochastic Optimization.

Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux

Proceedings of the International Conference on Machine Learning, 2023

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements.

Maryam Molamohammadi, Afaf Taïk, Nicolas Le Roux, Golnoosh Farnadi

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

2022

Multi-Head Adapter Routing for Data-Efficient Fine-Tuning.

Lucas Caccia, Edoardo Maria Ponti, Lucas Liu, Matheus Pereira, Nicolas Le Roux, Alessandro Sordoni

CoRR, 2022

A general class of surrogate functions for stable and efficient reinforcement learning.

Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging.

Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A functional mirror ascent view of policy gradient methods with function approximation.

Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux

CoRR, 2021

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging.

Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan

CoRR, 2021

Bridging the Gap Between Adversarial Robustness and Optimization Bias.

Fartash Faghri, Cristina Nader Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux

CoRR, 2021

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization.

Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux

Proceedings of the 38th International Conference on Machine Learning, 2021

Batch Reinforcement Learning Through Continuation Method.

Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed H. Chi, Honglak Lee, Minmin Chen

Proceedings of the 9th International Conference on Learning Representations, 2021

Impact of Aliasing on Generalization in Deep Convolutional Networks.

Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

An Effective Anti-Aliasing Approach for Residual Networks.

Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux, Ross Goroshin

CoRR, 2020

To Each Optimizer a Norm, To Each Norm its Generalization.

Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux

CoRR, 2020

The Geometry of Sign Gradient Descent.

Lukas Balles, Fabian Pedregosa, Nicolas Le Roux

CoRR, 2020

An operator view of policy gradient methods.

Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the interplay between noise and curvature and its effect on optimization and generalization.

Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Information matrices and generalization.

Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

CoRR, 2019

Anytime Tail Averaging.

Nicolas Le Roux

CoRR, 2019

A Geometric Perspective on Optimal Representations for Reinforcement Learning.

Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reducing the variance in online optimization by transporting past gradients.

Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Value Function Polytope in Reinforcement Learning.

Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans

Proceedings of the 36th International Conference on Machine Learning, 2019

Understanding the Impact of Entropy on Policy Optimization.

Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans

Proceedings of the 36th International Conference on Machine Learning, 2019

Distributional reinforcement learning with linear function approximation.

Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Online variance-reducing optimization.

Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol

Proceedings of the 6th International Conference on Learning Representations, 2018

Negative eigenvalues of the Hessian in deep neural networks.

Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol

Proceedings of the 6th International Conference on Learning Representations, 2018

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods.

Robert M. Gower, Nicolas Le Roux, Francis R. Bach

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Erratum to: Minimizing finite sums with the stochastic average gradient.

Mark Schmidt, Nicolas Le Roux, Francis R. Bach

Math. Program., 2017

Minimizing finite sums with the stochastic average gradient.

Mark Schmidt, Nicolas Le Roux, Francis R. Bach

Math. Program., 2017

A comparative study of counterfactual estimators.

Thomas Nedelec, Nicolas Le Roux, Vianney Perchet

CoRR, 2017

Distributed SAGA: Maintaining linear convergence rate with limited communication.

Clément Calauzènes, Nicolas Le Roux

CoRR, 2017

Tighter bounds lead to improved classifiers.

Nicolas Le Roux

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Efficient iterative policy optimization.

Nicolas Le Roux

CoRR, 2016

2015

Large-Scale Real-Time Product Recommendation at Criteo.

Romain Lerallut, Diane Gasselin, Nicolas Le Roux

Proceedings of the 9th ACM Conference on Recommender Systems, 2015

2013

Local Component Analysis

Nicolas Le Roux, Francis R. Bach

Proceedings of the 1st International Conference on Learning Representations, 2013

2012

A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets

Nicolas Le Roux, Mark Schmidt, Francis R. Bach

CoRR, 2012

A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets.

Nicolas Le Roux, Mark Schmidt, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

A latent factor model for highly multi-relational data.

Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Learning a Generative Model of Images by Factoring Appearance and Shape.

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn

Neural Comput., 2011

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization.

Mark Schmidt, Nicolas Le Roux, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Ask the locals: Multi-way local pooling for image recognition.

Y-Lan Boureau, Nicolas Le Roux, Francis R. Bach, Jean Ponce, Yann LeCun

Proceedings of the IEEE International Conference on Computer Vision, 2011

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs.

Nicolas Heess, Nicolas Le Roux, John M. Winn

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

2010

Deep Belief Networks Are Compact Universal Approximators.

Nicolas Le Roux, Yoshua Bengio

Neural Comput., 2010

A fast natural Newton method.

Nicolas Le Roux, Andrew W. Fitzgibbon

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2008

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks.

Nicolas Le Roux, Yoshua Bengio

Neural Comput., 2008

Products of ordinary differential operators by evaluation and interpolation.

Alin Bostan, Frédéric Chyzak, Nicolas Le Roux

Proceedings of the Symbolic and Algebraic Computation, International Symposium, 2008

2007

Continuous Neural Networks.

Nicolas Le Roux, Yoshua Bengio

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Topmoumoute Online Natural Gradient Algorithm.

Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Learning the 2-D Topology of Images.

Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Rank reduction of a class of pfaffian systems in two variables.

Nicolas Le Roux, Moulay A. Barkatou

Proceedings of the Symbolic and Algebraic Computation, International Symposium, 2006

Spectral Dimensionality Reduction.

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux, Jean-François Paiement, Pascal Vincent, Marie Ouimet

Proceedings of the Feature Extraction - Foundations and Applications, 2006

Large-Scale Algorithms.

Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux

Proceedings of the Semi-Supervised Learning, 2006

Label Propagation and Quadratic Criterion.

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux

Proceedings of the Semi-Supervised Learning, 2006

2005

Convex Neural Networks.

Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

The Curse of Highly Variable Functions for Local Kernel Machines.

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Efficient Non-Parametric Function Induction in Semi-Supervised Learning.

Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Learning Eigenfunctions Links Spectral Embedding and Kernel PCA.

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux, Jean-François Paiement, Pascal Vincent, Marie Ouimet

Neural Comput., 2004

2003

Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering.

Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux, Marie Ouimet

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Computing power series solutions of a nonlinear PDE system.

Evelyne Hubert, Nicolas Le Roux

Proceedings of the Symbolic and Algebraic Computation, 2003