List of publications

2013

Minimizing Finite Sums with the Stochastic Average Gradient
M. Schmidt, N. Le Roux and F. Bach
arXiv:1309.2388. [Code]

Local Component Analysis
N. Le Roux and F. Bach
ICLR, 2013. [Code]

2012

Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition
M. Schmidt and N. Le Roux
arXiv:1308.6370

A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets
N. Le Roux, M. Schmidt and F. Bach
NIPS 25, 2012. [arXiv version, Code, Slides, Poster]

A latent factor model for highly multi-relational data
R. Jenatton*, N. Le Roux*, A. Bordes and G. Obozinski
NIPS 25, 2012. [Website, Poster] (*Equal contribution)

2011

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
M. Schmidt, N. Le Roux and F. Bach
NIPS 24, 2011. [arXiv version]

Improving first and second-order methods by modeling uncertainty
N. Le Roux, Y. Bengio and A. Fitzgibbon
Chapter of Optimization for Machine Learning, MIT Press, Cambridge, MA, USA, 2011
Edited by S. Sra, S. Nowozin and S.J. Wright

Ask the locals: multi-way local pooling for image recognition
Y.-L. Boureau, N. Le Roux, F. Bach, J. Ponce and Y. LeCun
ICCV, 2011.

Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs
N. Heess, N. Le Roux and J.Winn
ICANN, 2011.

Learning a generative model of images by factoring appearance and shape
N. Le Roux, N. Heess, J. Shotton and J. Winn
Neural Computation, March 2011, Vol. 23, No. 3, Pages 593-650

2010

A fast natural Newton method
N. Le Roux and A.Fitzgibbon
ICML, 2010.

Deep Belief Networks are Compact Universal Approximators
N. Le Roux and Yoshua Bengio
Neural Computation, August 2010, Vol. 22, No. 8, Pages 2192-2207

2008

Avancées théoriques sur la représentation et l'optimisation des réseaux de neurones
N. Le Roux
PhD thesis, University of Montreal, Canada, 2008.

Topmoumoute Online Natural Gradient Algorithm
N. Le Roux, P.A. Manzagol and Y. Bengio
NIPS 20, 2008.

Learning the 2-D Topology of Images
N. Le Roux, Y. Bengio, P. Lamblin, M. Joliveau and B. Kegl
NIPS 20, 2008.

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
N. Le Roux and Y. Bengio
Neural Computation, June 2008, Vol. 20, No. 6, Pages 1631-1649

2007

Continuous Neural Networks
N. Le Roux and Y. Bengio
AISTATS 11, 2007.

2006

The Curse of Highly Variable Functions for Local Kernel Machines
Y. Bengio, O. Delalleau, and N. Le Roux
NIPS 18, 2006.

Convex Neural Networks
Y. Bengio, N. Le Roux, P. Vincent, O. Delalleau and P. Marcotte
NIPS 18, 2006.

Spectral Dimensionality Reduction
Y. Bengio, O. Delalleau, N. Le Roux, J.-F. Paiement, P. Vincent and M. Ouimet
Chapter of Feature Extraction, Foundations and Applications, Physica-Verlag, Springer, 2006
Edited by I. Guyon et al.

Label propagation and quadratic criterion
Y. Bengio, O. Delalleau and N. Le Roux
Chapter of Semi-supervised learning, MIT Press, Cambridge, MA, USA, 2006
Edited by O. Chapelle et al.

Large-scale algorithms
O. Delalleau Y. Bengio, and N. Le Roux
Chapter of Semi-supervised learning, MIT Press, Cambridge, MA, USA, 2006
Edited by O. Chapelle et al.

2005

Efficient Non-Parametric Function Induction in Semi-Supervised Learning
O. Delalleau, Y. Bengio, and N. Le Roux
AISTATS 10, 2005.

2004

Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering
Y. Bengio, J.-F. Paiement, P. Vincent, O. Delalleau, N. Le Roux and M. Ouimet
NIPS 16, 2004.

Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Y. Bengio, O. Delalleau, N. Le Roux, J.-F. Paiement, M. Ouimet and P. Vincent
Neural Computation, October 2004, Vol. 16, No. 10, Pages 2197-2219