Computing power series solutions of a nonlinear PDE system. - Evelyne Hubert; Nicolas Le Roux (ISSAC, 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 (NIPS, 2003)

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)

Efficient Non-Parametric Function Induction in Semi-Supervised Learning. - Olivier Delalleau; Yoshua Bengio; Nicolas Le Roux (AISTATS, 2005)

The Curse of Highly Variable Functions for Local Kernel Machines. - Yoshua Bengio; Olivier Delalleau; Nicolas Le Roux (NIPS, 2005)

Convex Neural Networks. - Yoshua Bengio; Nicolas Le Roux; Pascal Vincent; Olivier Delalleau; Patrice Marcotte (NIPS, 2005)

Label Propagation and Quadratic Criterion. - Yoshua Bengio; Olivier Delalleau; Nicolas Le Roux (Semi-Supervised Learning, 2006)

Large-Scale Algorithms. - Olivier Delalleau; Yoshua Bengio; Nicolas Le Roux (Semi-Supervised Learning, 2006)

Spectral Dimensionality Reduction. - Yoshua Bengio; Olivier Delalleau; Nicolas Le Roux; Jean-François Paiement; Pascal Vincent; Marie Ouimet (Feature Extraction, 2006)

Rank reduction of a class of pfaffian systems in two variables. - Nicolas Le Roux; Moulay A. Barkatou (ISSAC, 2006)

Learning the 2-D Topology of Images. - Nicolas Le Roux; Yoshua Bengio; Pascal Lamblin; Marc Joliveau; Balázs Kégl (NIPS, 2007)

Topmoumoute Online Natural Gradient Algorithm. - Nicolas Le Roux; Pierre-Antoine Manzagol; Yoshua Bengio (NIPS, 2007)

Continuous Neural Networks. - Nicolas Le Roux; Yoshua Bengio (AISTATS, 2007)

Products of ordinary differential operators by evaluation and interpolation. - Alin Bostan; Frédéric Chyzak; Nicolas Le Roux (ISSAC, 2008)

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. - Nicolas Le Roux; Yoshua Bengio (Neural Comput., 2008)

A fast natural Newton method. - Nicolas Le Roux; Andrew W. Fitzgibbon (ICML, 2010)

Deep Belief Networks Are Compact Universal Approximators. - Nicolas Le Roux; Yoshua Bengio (Neural Comput., 2010)

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. - Nicolas Heess; Nicolas Le Roux; John M. Winn (ICANN (2), 2011)

Ask the locals: Multi-way local pooling for image recognition. - Y-Lan Boureau; Nicolas Le Roux; Francis R. Bach; Jean Ponce; Yann LeCun (ICCV, 2011)

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. - Mark Schmidt 001; Nicolas Le Roux; Francis R. Bach (NIPS, 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)

A latent factor model for highly multi-relational data. - Rodolphe Jenatton; Nicolas Le Roux; Antoine Bordes; Guillaume Obozinski (NIPS, 2012)

A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. - Nicolas Le Roux; Mark Schmidt 001; Francis R. Bach (NIPS, 2012)

A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets - Nicolas Le Roux; Mark Schmidt 001; Francis R. Bach (CoRR, 2012)

Local Component Analysis - Nicolas Le Roux; Francis R. Bach (ICLR (Poster), 2013)

Large-Scale Real-Time Product Recommendation at Criteo. - Romain Lerallut; Diane Gasselin; Nicolas Le Roux (RecSys, 2015)

Efficient iterative policy optimization. - Nicolas Le Roux (CoRR, 2016)

Tighter bounds lead to improved classifiers. - Nicolas Le Roux (ICLR (Poster), 2017)

Distributed SAGA: Maintaining linear convergence rate with limited communication. - Clément Calauzènes; Nicolas Le Roux (CoRR, 2017)

A comparative study of counterfactual estimators. - Thomas Nedelec; Nicolas Le Roux; Vianney Perchet (CoRR, 2017)

Minimizing finite sums with the stochastic average gradient. - Mark Schmidt 001; Nicolas Le Roux; Francis R. Bach (Math. Program., 2017)

Erratum to: Minimizing finite sums with the stochastic average gradient. - Mark Schmidt 001; Nicolas Le Roux; Francis R. Bach (Math. Program., 2017)

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. - Robert M. Gower; Nicolas Le Roux; Francis R. Bach (AISTATS, 2018)

Negative eigenvalues of the Hessian in deep neural networks. - Guillaume Alain; Nicolas Le Roux; Pierre-Antoine Manzagol (ICLR (Workshop), 2018)

Online variance-reducing optimization. - Nicolas Le Roux; Reza Babanezhad 001; Pierre-Antoine Manzagol (ICLR (Workshop), 2018)

Distributional reinforcement learning with linear function approximation. - Marc G. Bellemare; Nicolas Le Roux; Pablo Samuel Castro; Subhodeep Moitra (AISTATS, 2019)

Understanding the Impact of Entropy on Policy Optimization. - Zafarali Ahmed; Nicolas Le Roux; Mohammad Norouzi 002; Dale Schuurmans (ICML, 2019)

The Value Function Polytope in Reinforcement Learning. - Robert Dadashi; Marc G. Bellemare; Adrien Ali Taïga; Nicolas Le Roux; Dale Schuurmans (ICML, 2019)

Reducing the variance in online optimization by transporting past gradients. - Sébastien M. R. Arnold; Pierre-Antoine Manzagol; Reza Babanezhad 001; Ioannis Mitliagkas; Nicolas Le Roux (NeurIPS, 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 (NeurIPS, 2019)

Anytime Tail Averaging. - Nicolas Le Roux (CoRR, 2019)

Information matrices and generalization. - Valentin Thomas; Fabian Pedregosa; Bart van Merriënboer; Pierre-Antoine Manzagol; Yoshua Bengio; Nicolas Le Roux (CoRR, 2019)

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 (AISTATS, 2020)

An operator view of policy gradient methods. - Dibya Ghosh; Marlos C. Machado; Nicolas Le Roux (NeurIPS, 2020)

The Geometry of Sign Gradient Descent. - Lukas Balles; Fabian Pedregosa; Nicolas Le Roux (CoRR, 2020)

To Each Optimizer a Norm, To Each Norm its Generalization. - Sharan Vaswani; Reza Babanezhad 001; Jose Gallego; Aaron Mishkin; Simon Lacoste-Julien; Nicolas Le Roux (CoRR, 2020)

An Effective Anti-Aliasing Approach for Residual Networks. - Cristina Nader Vasconcelos; Hugo Larochelle; Vincent Dumoulin; Nicolas Le Roux; Ross Goroshin (CoRR, 2020)

Impact of Aliasing on Generalization in Deep Convolutional Networks. - Cristina Nader Vasconcelos; Hugo Larochelle; Vincent Dumoulin; Rob Romijnders; Nicolas Le Roux; Ross Goroshin (ICCV, 2021)

Batch Reinforcement Learning Through Continuation Method. - Yijie Guo; Shengyu Feng; Nicolas Le Roux; Ed H. Chi; Honglak Lee; Minmin Chen (ICLR, 2021)

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. - Wesley Chung; Valentin Thomas; Marlos C. Machado; Nicolas Le Roux (ICML, 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)

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (CoRR, 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 using Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (AISTATS, 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 (AISTATS, 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)

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements. - Maryam Molamohammadi; Afaf Taïk; Nicolas Le Roux; Golnoosh Farnadi (EAAMO, 2023)

Target-based Surrogates for Stochastic Optimization. - Jonathan Wilder Lavington; Sharan Vaswani; Reza Babanezhad Harikandeh; Mark Schmidt 001; Nicolas Le Roux (ICML, 2023)

Multi-Head Adapter Routing for Cross-Task Generalization. - Lucas Page-Caccia; Edoardo Maria Ponti; Zhan Su 002; Matheus Pereira; Nicolas Le Roux; Alessandro Sordoni (NeurIPS, 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 (NeurIPS, 2023)

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. - Sharan Vaswani; Amirreza Kazemi; Reza Babanezhad Harikandeh; Nicolas Le Roux (NeurIPS, 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)

Language-guided Skill Learning with Temporal Variational Inference. - Haotian Fu; Pratyusha Sharma; Elias Stengel-Eskin; George Konidaris 001; Nicolas Le Roux; Marc-Alexandre Côté; Xingdi Yuan (ICML, 2024)

Towards Modular LLMs by Building and Reusing a Library of LoRAs. - Oleksiy Ostapenko; Zhan Su 002; Edoardo M. Ponti; Laurent Charlin; Nicolas Le Roux; Lucas Caccia; Alessandro Sordoni (ICML, 2024)

Improving Context-Aware Preference Modeling for Language Models. - Silviu Pitis; Ziang Xiao; Nicolas Le Roux; Alessandro Sordoni (NeurIPS, 2024)

Towards Modular LLMs by Building and Reusing a Library of LoRAs. - Oleksiy Ostapenko; Zhan Su 002; Edoardo Maria Ponti; Laurent Charlin; Nicolas Le Roux; Matheus Pereira; Lucas Caccia; Alessandro Sordoni (CoRR, 2024)

VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment. - Amirhossein Kazemnejad; Milad Aghajohari; Eva Portelance; Alessandro Sordoni; Siva Reddy; Aaron C. Courville; Nicolas Le Roux (CoRR, 2024)

fPLSA: Learning Semantic Structures in Document Collections Using Foundation Models. - Weijia Xu; Nebojsa Jojic; Nicolas Le Roux (CoRR, 2024)

Fast Convergence of Softmax Policy Mirror Ascent. - Reza Asad; Reza Babanezhad Harikandeh; Issam H. Laradji; Nicolas Le Roux; Sharan Vaswani (AISTATS, 2025)

Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs. - Nicolas Le Roux; Marc G. Bellemare; Jonathan Lebensold; Arnaud Bergeron; Joshua Greaves; Alexandre Fréchette; Carolyne Pelletier; Eric Thibodeau-Laufer; Sándor Tóth; Sam Work (CoRR, 2025)