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Nicolas Le Roux
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2020 – today
- 2024
- [c41]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. ICML 2024 - [c40]Oleksiy Ostapenko, Zhan Su, Edoardo M. Ponti, Laurent Charlin, Nicolas Le Roux, Lucas Caccia, Alessandro Sordoni:
Towards Modular LLMs by Building and Reusing a Library of LoRAs. ICML 2024 - [i37]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 abs/2402.16354 (2024) - [i36]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 abs/2405.11157 (2024) - [i35]Silviu Pitis, Ziang Xiao, Nicolas Le Roux, Alessandro Sordoni:
Improving Context-Aware Preference Modeling for Language Models. CoRR abs/2407.14916 (2024) - [i34]Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron C. Courville, Nicolas Le Roux:
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment. CoRR abs/2410.01679 (2024) - [i33]Weijia Xu, Nebojsa Jojic, Nicolas Le Roux:
fPLSA: Learning Semantic Structures in Document Collections Using Foundation Models. CoRR abs/2410.05481 (2024) - 2023
- [c39]Maryam Molamohammadi
, Afaf Taïk
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements. EAAMO 2023: 33:1-33:12 - [c38]Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux:
Target-based Surrogates for Stochastic Optimization. ICML 2023: 18614-18651 - [c37]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 - [c36]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 - [c35]Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux:
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. NeurIPS 2023 - [i32]Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux:
Target-based Surrogates for Stochastic Optimization. CoRR abs/2302.02607 (2023) - [i31]Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad, Nicolas Le Roux:
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. CoRR abs/2305.15249 (2023) - [i30]Maryam Molamohammadi, Afaf Taïk, Nicolas Le Roux, Golnoosh Farnadi:
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements. CoRR abs/2306.10043 (2023) - [i29]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 abs/2306.12509 (2023) - 2022
- [c34]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS 2022: 8619-8649 - [c33]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: 9793-9826 - [i28]Lucas Caccia, Edoardo Maria Ponti, Lucas Liu, Matheus Pereira, Nicolas Le Roux, Alessandro Sordoni:
Multi-Head Adapter Routing for Data-Efficient Fine-Tuning. CoRR abs/2211.03831 (2022) - 2021
- [c32]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin:
Impact of Aliasing on Generalization in Deep Convolutional Networks. ICCV 2021: 10509-10518 - [c31]Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed H. Chi, Honglak Lee, Minmin Chen:
Batch Reinforcement Learning Through Continuation Method. ICLR 2021 - [c30]Wesley Chung, Valentin Thomas, Marlos C. Machado
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. ICML 2021: 1999-2009 - [i27]Fartash Faghri, Cristina Nader Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux:
Bridging the Gap Between Adversarial Robustness and Optimization Bias. CoRR abs/2102.08868 (2021) - [i26]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 abs/2107.00464 (2021) - [i25]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin:
Impact of Aliasing on Generalization in Deep Convolutional Networks. CoRR abs/2108.03489 (2021) - [i24]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 abs/2108.05828 (2021) - 2020
- [c29]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: 3503-3513 - [c28]Dibya Ghosh, Marlos C. Machado
An operator view of policy gradient methods. NeurIPS 2020 - [i23]Lukas Balles, Fabian Pedregosa, Nicolas Le Roux:
The Geometry of Sign Gradient Descent. CoRR abs/2002.08056 (2020) - [i22]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 abs/2006.06821 (2020) - [i21]Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux:
An operator view of policy gradient methods. CoRR abs/2006.11266 (2020) - [i20]Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux:
Beyond variance reduction: Understanding the true impact of baselines on policy optimization. CoRR abs/2008.13773 (2020) - [i19]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux, Ross Goroshin:
An Effective Anti-Aliasing Approach for Residual Networks. CoRR abs/2011.10675 (2020)
2010 – 2019
- 2019
- [c27]Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. AISTATS 2019: 2203-2211 - [c26]Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans:
Understanding the Impact of Entropy on Policy Optimization. ICML 2019: 151-160 - [c25]Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans:
The Value Function Polytope in Reinforcement Learning. ICML 2019: 1486-1495 - [c24]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: 4360-4371 - [c23]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: 5392-5403 - [i18]Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare:
The Value Function Polytope in Reinforcement Learning. CoRR abs/1901.11524 (2019) - [i17]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 abs/1901.11530 (2019) - [i16]Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol:
Negative eigenvalues of the Hessian in deep neural networks. CoRR abs/1902.02366 (2019) - [i15]Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. CoRR abs/1902.03149 (2019) - [i14]Nicolas Le Roux:
Anytime Tail Averaging. CoRR abs/1902.05083 (2019) - [i13]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 abs/1906.03532 (2019) - [i12]Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux:
Information matrices and generalization. CoRR abs/1906.07774 (2019) - 2018
- [c22]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: 707-715 - [c21]Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol:
Negative eigenvalues of the Hessian in deep neural networks. ICLR (Workshop) 2018 - [c20]Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol:
Online variance-reducing optimization. ICLR (Workshop) 2018 - [i11]Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans:
Understanding the impact of entropy on policy optimization. CoRR abs/1811.11214 (2018) - 2017
- [j6]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1-2): 83-112 (2017) - [j5]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Erratum to: Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1-2): 113 (2017) - [c19]Nicolas Le Roux:
Tighter bounds lead to improved classifiers. ICLR (Poster) 2017 - [i10]Thomas Nedelec, Nicolas Le Roux, Vianney Perchet:
A comparative study of counterfactual estimators. CoRR abs/1704.00773 (2017) - [i9]Clément Calauzènes, Nicolas Le Roux:
Distributed SAGA: Maintaining linear convergence rate with limited communication. CoRR abs/1705.10405 (2017) - [i8]Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. CoRR abs/1710.07462 (2017) - 2016
- [i7]Nicolas Le Roux:
Tighter bounds lead to improved classifiers. CoRR abs/1606.09202 (2016) - [i6]Nicolas Le Roux:
Efficient iterative policy optimization. CoRR abs/1612.08967 (2016) - 2015
- [c18]Romain Lerallut, Diane Gasselin, Nicolas Le Roux:
Large-Scale Real-Time Product Recommendation at Criteo. RecSys 2015: 232 - 2013
- [c17]Nicolas Le Roux, Francis R. Bach:
Local Component Analysis. ICLR (Poster) 2013 - [i5]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Minimizing Finite Sums with the Stochastic Average Gradient. CoRR abs/1309.2388 (2013) - 2012
- [c16]Nicolas Le Roux, Mark Schmidt, Francis R. Bach:
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. NIPS 2012: 2672-2680 - [c15]Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski:
A latent factor model for highly multi-relational data. NIPS 2012: 3176-3184 - [i4]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 abs/1202.6258 (2012) - 2011
- [j4]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): 593-650 (2011) - [c14]Nicolas Heess, Nicolas Le Roux, John M. Winn:
Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) 2011: 9-16 - [c13]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: 2651-2658 - [c12]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS 2011: 1458-1466 - [i3]Nicolas Heess, Nicolas Le Roux, John M. Winn:
Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs. CoRR abs/1107.3823 (2011) - [i2]Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. CoRR abs/1109.2415 (2011) - 2010
- [j3]Nicolas Le Roux, Yoshua Bengio:
Deep Belief Networks Are Compact Universal Approximators. Neural Comput. 22(8): 2192-2207 (2010) - [c11]Nicolas Le Roux, Andrew W. Fitzgibbon:
A fast natural Newton method. ICML 2010: 623-630
2000 – 2009
- 2008
- [j2]Nicolas Le Roux, Yoshua Bengio:
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. Neural Comput. 20(6): 1631-1649 (2008) - [c10]Alin Bostan, Frédéric Chyzak, Nicolas Le Roux:
Products of ordinary differential operators by evaluation and interpolation. ISSAC 2008: 23-30 - [i1]Alin Bostan, Frédéric Chyzak, Nicolas Le Roux:
Products of Ordinary Differential Operators by Evaluation and Interpolation. CoRR abs/0804.2181 (2008) - 2007
- [c9]Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl:
Learning the 2-D Topology of Images. NIPS 2007: 841-848 - [c8]Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio:
Topmoumoute Online Natural Gradient Algorithm. NIPS 2007: 849-856 - [c7]Nicolas Le Roux, Yoshua Bengio:
Continuous Neural Networks. AISTATS 2007: 404-411 - 2006
- [c6]Nicolas Le Roux, Moulay A. Barkatou:
Rank reduction of a class of pfaffian systems in two variables. ISSAC 2006: 204-211 - [p3]Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
Label Propagation and Quadratic Criterion. Semi-Supervised Learning 2006: 192-216 - [p2]Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux:
Large-Scale Algorithms. Semi-Supervised Learning 2006: 332-341 - [p1]Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux, Jean-François Paiement, Pascal Vincent, Marie Ouimet:
Spectral Dimensionality Reduction. Feature Extraction 2006: 519-550 - 2005
- [c5]Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux:
Efficient Non-Parametric Function Induction in Semi-Supervised Learning. AISTATS 2005: 96-103 - [c4]Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
The Curse of Highly Variable Functions for Local Kernel Machines. NIPS 2005: 107-114 - [c3]Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte:
Convex Neural Networks. NIPS 2005: 123-130 - 2004
- [j1]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): 2197-2219 (2004) - 2003
- [c2]Evelyne Hubert, Nicolas Le Roux:
Computing power series solutions of a nonlinear PDE system. ISSAC 2003: 148-155 - [c1]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: 177-184
Coauthor Index
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