# Nicolas Le Roux

According to our database

Collaborative distances:

^{1}, Nicolas Le Roux authored at least 32 papers between 2003 and 2019.Collaborative distances:

## Timeline

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## Bibliography

2019

The Value Function Polytope in Reinforcement Learning.

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

Understanding the Impact of Entropy on Policy Optimization.

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

Distributional reinforcement learning with linear function approximation.

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

2018

Online variance-reducing optimization.

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

Negative eigenvalues of the Hessian in deep neural networks.

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

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

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

2017

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

Math. Program., 2017

Minimizing finite sums with the stochastic average gradient.

Math. Program., 2017

Tighter bounds lead to improved classifiers.

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

2015

Large-Scale Real-Time Product Recommendation at Criteo.

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

2013

Local Component Analysis

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

2012

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

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.

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.

Neural Computation, 2011

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

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.

Proceedings of the IEEE International Conference on Computer Vision, 2011

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

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

2010

Deep Belief Networks Are Compact Universal Approximators.

Neural Computation, 2010

A fast natural Newton method.

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

2008

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks.

Neural Computation, 2008

Products of ordinary differential operators by evaluation and interpolation.

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

2007

Continuous Neural Networks.

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

Topmoumoute Online Natural Gradient Algorithm.

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

Learning the 2-D Topology of Images.

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

2006

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

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

Large-Scale Algorithms.

Proceedings of the Semi-Supervised Learning, 2006

Label Propagation and Quadratic Criterion.

Proceedings of the Semi-Supervised Learning, 2006

2005

Convex Neural Networks.

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.

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

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

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

2004

Learning Eigenfunctions Links Spectral Embedding and Kernel PCA.

Neural Computation, 2004

2003

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

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