Nicolas Le Roux

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 53

Top Topics

Policy Gradient

Function Approximation

Reinforcement Learning

Top Venues

Publications

Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin, George Konidaris, Nicolas Le Roux, Marc-Alexandre Côté, Xingdi Yuan
Language-guided Skill Learning with Temporal Variational Inference. CoRR (2024)
Silviu Pitis, Ziang Xiao, Nicolas Le Roux, Alessandro Sordoni
Improving Context-Aware Preference Modeling for Language Models. CoRR (2024)
Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti, Laurent Charlin, Nicolas Le Roux, Matheus Pereira, Lucas Caccia, Alessandro Sordoni
Towards Modular LLMs by Building and Reusing a Library of LoRAs. CoRR (2024)
Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. NeurIPS (2023)
Lucas Page-Caccia, Edoardo Maria Ponti, Zhan Su, Matheus Pereira, Nicolas Le Roux, Alessandro Sordoni
Multi-Head Adapter Routing for Cross-Task Generalization. NeurIPS (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. CoRR (2023)
Maryam Molamohammadi, Afaf Taïk, Nicolas Le Roux, Golnoosh Farnadi
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements. EAAMO (2023)
Alessandro Sordoni, Eric Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, Nicolas Le Roux
Joint Prompt Optimization of Stacked LLMs using Variational Inference. NeurIPS (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. ICML (2023)
Maryam Molamohammadi, Afaf Taïk, Nicolas Le Roux, Golnoosh Farnadi
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements. CoRR (2023)
Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, Nicolas Le Roux
Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference. CoRR (2023)
Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad, Nicolas Le Roux
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. CoRR (2023)
Lucas Caccia, Edoardo Maria Ponti, Lucas Liu, Matheus Pereira, Nicolas Le Roux, Alessandro Sordoni
Multi-Head Adapter Routing for Data-Efficient Fine-Tuning. CoRR (2022)
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS (2022)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS (2022)
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin
Impact of Aliasing on Generalization in Deep Convolutional Networks. ICCV (2021)
Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. ICML (2021)
Fartash Faghri, Cristina Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux
Bridging the Gap Between Adversarial Robustness and Optimization Bias. CoRR (2021)
Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen
Batch Reinforcement Learning Through Continuation Method. ICLR (2021)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR (2021)
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
A functional mirror ascent view of policy gradient methods with function approximation. CoRR (2021)
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin
Impact of Aliasing on Generalization in Deep Convolutional Networks. CoRR (2021)
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux
An operator view of policy gradient methods. NeurIPS (2020)
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux, Ross Goroshin
An Effective Anti-Aliasing Approach for Residual Networks. CoRR (2020)
Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux
Beyond variance reduction: Understanding the true impact of baselines on policy optimization. CoRR (2020)
Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux
On the interplay between noise and curvature and its effect on optimization and generalization. AISTATS (2020)
Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux
To Each Optimizer a Norm, To Each Norm its Generalization. CoRR (2020)
Lukas Balles, Fabian Pedregosa, Nicolas Le Roux
The Geometry of Sign Gradient Descent. CoRR (2020)
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux
An operator view of policy gradient methods. CoRR (2020)
Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans
The Value Function Polytope in Reinforcement Learning. ICML (2019)
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans
Understanding the Impact of Entropy on Policy Optimization. ICML (2019)
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra
Distributional reinforcement learning with linear function approximation. AISTATS (2019)
Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux
Information matrices and generalization. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. NeurIPS (2019)
Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare
The Value Function Polytope in Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. CoRR (2019)
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol
Negative eigenvalues of the Hessian in deep neural networks. CoRR (2019)
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra
Distributional reinforcement learning with linear function approximation. CoRR (2019)
Nicolas Le Roux
Anytime Tail Averaging. CoRR (2019)
Robert M. Gower, Nicolas Le Roux, Francis R. Bach
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. AISTATS (2018)
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans
Understanding the impact of entropy on policy optimization. CoRR (2018)
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol
Online variance-reducing optimization. ICLR (Workshop) (2018)
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol
Negative eigenvalues of the Hessian in deep neural networks. ICLR (Workshop) (2018)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Erratum to: Minimizing finite sums with the stochastic average gradient. Math. Program. 162 (1-2) (2017)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Minimizing finite sums with the stochastic average gradient. Math. Program. 162 (1-2) (2017)
Robert M. Gower, Nicolas Le Roux, Francis R. Bach
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. CoRR (2017)
Clément Calauzènes, Nicolas Le Roux
Distributed SAGA: Maintaining linear convergence rate with limited communication. CoRR (2017)
Nicolas Le Roux
Tighter bounds lead to improved classifiers. ICLR (Poster) (2017)
Thomas Nedelec, Nicolas Le Roux, Vianney Perchet
A comparative study of counterfactual estimators. CoRR (2017)
Nicolas Le Roux
Tighter bounds lead to improved classifiers. CoRR (2016)
Nicolas Le Roux
Efficient iterative policy optimization. CoRR (2016)
Romain Lerallut, Diane Gasselin, Nicolas Le Roux
Large-Scale Real-Time Product Recommendation at Criteo. RecSys (2015)
Nicolas Le Roux, Francis R. Bach
Local Component Analysis ICLR (Poster) (2013)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Minimizing Finite Sums with the Stochastic Average Gradient. CoRR (2013)
Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski
A latent factor model for highly multi-relational data. NIPS (2012)
Nicolas Le Roux, Mark Schmidt, Francis R. Bach
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. NIPS (2012)
Nicolas Le Roux, Mark Schmidt, Francis R. Bach
A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets CoRR (2012)
Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn
Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Comput. 23 (3) (2011)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization CoRR (2011)
Mark Schmidt, Nicolas Le Roux, Francis R. Bach
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS (2011)
Y-Lan Boureau, Nicolas Le Roux, Francis R. Bach, Jean Ponce, Yann LeCun
Ask the locals: Multi-way local pooling for image recognition. ICCV (2011)
Nicolas Heess, Nicolas Le Roux, John M. Winn
Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) (2011)
Nicolas Heess, Nicolas Le Roux, John M. Winn
Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs CoRR (2011)
Nicolas Le Roux, Andrew W. Fitzgibbon
A fast natural Newton method. ICML (2010)
Nicolas Le Roux, Yoshua Bengio
Deep Belief Networks Are Compact Universal Approximators. Neural Comput. 22 (8) (2010)
Nicolas Le Roux, Yoshua Bengio
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. Neural Comput. 20 (6) (2008)
Alin Bostan, Frédéric Chyzak, Nicolas Le Roux
Products of Ordinary Differential Operators by Evaluation and Interpolation CoRR (2008)
Alin Bostan, Frédéric Chyzak, Nicolas Le Roux
Products of ordinary differential operators by evaluation and interpolation. ISSAC (2008)
Nicolas Le Roux, Yoshua Bengio
Continuous Neural Networks. AISTATS (2007)
Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
Learning the 2-D Topology of Images. NIPS (2007)
Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio
Topmoumoute Online Natural Gradient Algorithm. NIPS (2007)
Nicolas Le Roux, Moulay A. Barkatou
Rank reduction of a class of pfaffian systems in two variables. ISSAC (2006)
Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux
Efficient Non-Parametric Function Induction in Semi-Supervised Learning. AISTATS (2005)
Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux
The Curse of Highly Variable Functions for Local Kernel Machines. NIPS (2005)
Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte
Convex Neural Networks. NIPS (2005)
Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux, Jean-François Paiement, Pascal Vincent, Marie Ouimet
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA. Neural Comput. 16 (10) (2004)
Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux, Marie Ouimet
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. NIPS (2003)
Evelyne Hubert, Nicolas Le Roux
Computing power series solutions of a nonlinear PDE system. ISSAC (2003)