Leon Bottou

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Diplôme d'Ingénieur from the École Polytechnique (X84) in 1987, the Master of Mathematics, Applied Mathematics and Computer Science from Ecole Normale Supérieure in 1988, and a PhD in computer science from University of Paris-Sud in 1991 I went to AT & T Bell Laboratories, AT & T Labs, NEC Labs America, and Microsoft Research. I joined the Facebook AI Research in March 2015.

Featured Co-authors

Bernhard Schölkopf
126 publications
Francis Bach
117 publications
Yann LeCun
80 publications
Alekh Agarwal
45 publications
Jonas Peters
32 publications
David Grangier
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David Lopez-Paz
25 publications
Yann Ollivier
25 publications
Nicolas Usunier
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Rachel Ward
22 publications
Soumith Chintala
18 publications

On the Convergence of Adam and Adagrad

We provide a simple proof of the convergence of the optimization algorit...
03/05/2020 ∙ by Alexandre Défossez, et al. ∙ 19 ∙ share

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Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed

We study the problem of source separation for music using deep learning ...
09/03/2019 ∙ by Alexandre Défossez, et al. ∙ 5 ∙ share

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Invariant Risk Minimization

We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
07/05/2019 ∙ by Martin Arjovsky, et al. ∙ 1 ∙ share

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Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

We look at the eigenvalues of the Hessian of a loss function before and ...
11/22/2016 ∙ by Levent Sagun, et al. ∙ 0 ∙ share

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Diagonal Rescaling For Neural Networks

We define a second-order neural network stochastic gradient training alg...
05/25/2017 ∙ by Jean Lafond, et al. ∙ 0 ∙ share

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Wasserstein GAN

We introduce a new algorithm named WGAN, an alternative to traditional G...
01/26/2017 ∙ by Martin Arjovsky, et al. ∙ 0 ∙ share

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Towards Principled Methods for Training Generative Adversarial Networks

The goal of this paper is not to introduce a single algorithm or method,...
01/17/2017 ∙ by Martin Arjovsky, et al. ∙ 0 ∙ share

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Optimization Methods for Large-Scale Machine Learning

This paper provides a review and commentary on the past, present, and fu...
06/15/2016 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

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ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine present...
09/16/2014 ∙ by Patrice Simard, et al. ∙ 0 ∙ share

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Unifying distillation and privileged information

Distillation (Hinton et al., 2015) and privileged information (Vapnik & ...
11/11/2015 ∙ by David Lopez-Paz, et al. ∙ 0 ∙ share

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No Regret Bound for Extreme Bandits

Algorithms for hyperparameter optimization abound, all of which work wel...
08/12/2015 ∙ by Robert Nishihara, et al. ∙ 0 ∙ share

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Discovering Causal Signals in Images

This paper establishes the existence of observable footprints that revea...
05/26/2016 ∙ by David Lopez-Paz, et al. ∙ 0 ∙ share

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Counterfactual Reasoning and Learning Systems

This work shows how to leverage causal inference to understand the behav...
09/11/2012 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

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A Lower Bound for the Optimization of Finite Sums

This paper presents a lower bound for optimizing a finite sum of n funct...
10/02/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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From Machine Learning to Machine Reasoning

A plausible definition of "reasoning" could be "algebraically manipulati...
02/09/2011 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

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Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variet...
12/21/2017 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

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Adversarial Vulnerability of Neural Networks Increases With Input Dimension

Over the past four years, neural networks have proven vulnerable to adve...
02/05/2018 ∙ by Carl-Johann Simon-Gabriel, et al. ∙ 0 ∙ share

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WNGrad: Learn the Learning Rate in Gradient Descent

Adjusting the learning rate schedule in stochastic gradient methods is a...
03/07/2018 ∙ by Xiaoxia Wu, et al. ∙ 0 ∙ share

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AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization

Adaptive gradient methods such as AdaGrad and its variants update the st...
06/05/2018 ∙ by Rachel Ward, et al. ∙ 0 ∙ share

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Controlling Covariate Shift using Equilibrium Normalization of Weights

We introduce a new normalization technique that exhibits the fast conver...
12/11/2018 ∙ by Aaron Defazio, et al. ∙ 0 ∙ share

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On the Ineffectiveness of Variance Reduced Optimization for Deep Learning

The application of stochastic variance reduction to optimization has sho...
12/11/2018 ∙ by Aaron Defazio, et al. ∙ 0 ∙ share

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SING: Symbol-to-Instrument Neural Generator

Recent progress in deep learning for audio synthesis opens the way to mo...
10/23/2018 ∙ by Alexandre Défossez, et al. ∙ 0 ∙ share

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Cold Case: The Lost MNIST Digits

Although the popular MNIST dataset [LeCun et al., 1994] is derived from ...
05/25/2019 ∙ by Chhavi Yadav, et al. ∙ 0 ∙ share

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Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks

In this work, we describe a set of rules for the design and initializati...
06/10/2019 ∙ by Aaron Defazio, et al. ∙ 0 ∙ share

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Symplectic Recurrent Neural Networks

We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algo...
09/29/2019 ∙ by Zhengdao Chen, et al. ∙ 0 ∙ share

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Music Source Separation in the Waveform Domain

Source separation for music is the task of isolating contributions, or s...
11/27/2019 ∙ by Alexandre Défossez, et al. ∙ 0 ∙ share

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