Simon Lacoste-Julien

Mila > About Mila > Team > Simon Lacoste-Julien
Core Academic Member
Simon Lacoste-Julien
Associate Professor, Université de Montréal, Samsung
Simon Lacoste-Julien

Simon Lacoste-Julien is a an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal. His research interests are machine learning and applied math, with applications to computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.

Publications

2021-12

Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas and Simon Lacoste-Julien

2021-09

Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien and Andrea Lodi

2021-07

Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
Sébastien Lachapelle, Pau Rodríguez López, Rémi Le Priol, Alexandre Lacoste and Simon Lacoste-Julien
arXiv preprint arXiv:2107.10098
(2021-07-21)
ui.adsabs.harvard.eduPDF
Structured Convolutional Kernel Networks for Airline Crew Scheduling
Yassine Yaakoubi, Francois Soumis and Simon Lacoste-Julien
Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien and Damien Scieur

2021-05

Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)
Sharan Vaswani, Issam H. Laradji, Frederik Kunstner, Si Yi Meng, Mark Schmidt and Simon Lacoste-Julien
arXiv e-prints
(2021-05-04)
ui.adsabs.harvard.eduPDF
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
Namyeong Kwon, Hwidong Na, Gabriel Huang and Simon Lacoste-Julien

2021-03

Implicit Regularization via Neural Feature Alignment
Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent and Simon Lacoste-Julien
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji and Simon Lacoste-Julien
Online Adversarial Attacks.
Andjela Mladenovic, Avishek Joey Bose, Hugo Berard, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent and Gauthier Gidel
arXiv preprint arXiv:2103.02014
(2021-03-02)
ui.adsabs.harvard.eduPDF

2021-02

SVRG Meets AdaGrad: Painless Variance Reduction.
Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt and Simon Lacoste-Julien
arXiv preprint arXiv:2102.09645
(2021-02-18)
ui.adsabs.harvard.eduPDF

2020-12

Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation
Yassine Yaakoubi, François Soumis and Simon Lacoste-Julien

2020-11

Geometry-Aware Universal Mirror-Prox.
Reza Babanezhad and Simon Lacoste-Julien
arXiv preprint arXiv:2011.11203
(2020-11-23)
ui.adsabs.harvard.eduPDF
On the Convergence of Continuous Constrained Optimization for Structure Learning.
Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke and Simon Lacoste-Julien
arXiv preprint arXiv:2011.11150
(2020-11-23)
ui.adsabs.harvard.eduPDF

2020-09

Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
Yassine Yaakoubi, François Soumis and Simon Lacoste-Julien
arXiv preprint arXiv:2009.12501
(2020-09-26)
ui.adsabs.harvard.eduPDF

2020-08

Implicit Regularization in Deep Learning: A View from Function Space.
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent and Simon Lacoste-Julien
(venue unknown)
(2020-08-03)
www.microsoft.com

2020-07

Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien and Ioannis Mitliagkas
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien and Alexandre Drouin
Adversarial Example Games.
Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien and William L. Hamilton
arXiv preprint arXiv:2007.00720
(2020-07-01)
ui.adsabs.harvard.eduPDF

2020-06

To Each Optimizer a Norm, To Each Norm its Generalization.
Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien and Nicolas Le Roux
arXiv preprint arXiv:2006.06821
(2020-06-11)
ui.adsabs.harvard.eduPDF
GAIT: A Geometric Approach to Information Theory
Jose Gallego-Posada, Ankit Vani, Max Schwarzer and Simon Lacoste-Julien
AISTATS 2020
(2020-06-03)
proceedings.mlr.pressPDF
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.
AISTATS 2020
(2020-06-03)
proceedings.mlr.pressPDF
Accelerating Smooth Games by Manipulating Spectral Shapes.
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien and Gauthier Gidel
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation.
Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt and Simon Lacoste-Julien

2020-05

An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol, Reza Babanezhad, Yoshua Bengio and Simon Lacoste-Julien
AISTATS 2020
(2020-05-18)
proceedings.mlr.pressPDF
An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol, Reza Babanezhad Harikandeh, Yoshua Bengio and Simon Lacoste-Julien
arXiv preprint arXiv:2005.09136
(2020-05-18)
ui.adsabs.harvard.eduPDF

2020-04

Gradient-Based Neural DAG Learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu and Simon Lacoste-Julien
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent and Simon Lacoste-Julien

2020-01

Adversarial Example Games
Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien and Will Hamilton
NEURIPS 2020
(2020-01-01)
papers.nips.ccPDF

2019-09

Are Few-shot Learning Benchmarks Too Simple ?
(venue unknown)
(2019-09-25)
openreview.netPDF
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew Blaschko and Eugene Belilovsky
IEEE Transactions on Pattern Analysis and Machine Intelligence
(2019-09-01)
europepmc.orgPDF

2019-06

GEAR: Geometry-Aware Rényi Information.
Jose Gallego-Posada, Ankit Vani, Max Schwarzer and Simon Lacoste-Julien
(venue unknown)
(2019-06-19)
dblp.uni-trier.de
GAIT: A Geometric Approach to Information Theory
Jose Gallego, Ankit Vani, Max Schwarzer and Simon Lacoste-Julien
arXiv: Learning
(2019-06-19)
ui.adsabs.harvard.eduPDF
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games
Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien and Gauthier Gidel
arXiv: Learning
(2019-06-13)
ui.adsabs.harvard.eduPDF
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.
(venue unknown)
(2019-06-13)
dblp.uni-trier.dePDF

2019-05

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel and Simon Lacoste-Julien

2019-04

Negative Momentum for Improved Game Dynamics
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien and Ioannis Mitliagkas
AISTATS 2019
(2019-04-11)
proceedings.mlr.pressPDF
Flight-connection prediction for airline crew scheduling to construct initial clusters for OR optimizer
Yassine Yaakoubi, Simon Lacoste-Julien and François Soumis
Les Cahiers du GERAD
(2019-04-01)
www.gerad.ca

2019-02

Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time
arXiv preprint arXiv:1902.08605
(2019-02-22)
ui.adsabs.harvard.eduPDF
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification.
(venue unknown)
(2019-02-22)
dblp.uni-trier.dePDF

2019-01

Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
Reducing Noise in GAN Training with Variance Reduced Extragradient
Tatjana Chavdarova, Gauthier Gidel, François Fleuret and Simon Lacoste-Julien

2018-10

A Modern Take on the Bias-Variance Tradeoff in Neural Networks
Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien and Ioannis Mitliagkas
arXiv preprint arXiv:1810.08591
(2018-10-19)
ui.adsabs.harvard.eduPDF
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Kirill Struminsky, Simon Lacoste-Julien and Anton Osokin

2018-09

Learning from Narrated Instruction Videos
Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev and Simon Lacoste-Julien
IEEE Transactions on Pattern Analysis and Machine Intelligence
(2018-09-01)
europepmc.org

2018-08

Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
Rémi Le Priol, Alexandre Piché and Simon Lacoste-Julien
UAI 2018
(2018-08-06)
auai.orgPDF

2018-07

Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning.
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien and Andrea Lodi
(venue unknown)
(2018-07-31)
dblp.uni-trier.de
Negative Momentum for Improved Game Dynamics
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Remi Lepriol, Gabriel Huang, Simon Lacoste-Julien and Ioannis Mitliagkas
arXiv preprint arXiv:1807.04740
(2018-07-12)
ui.adsabs.harvard.eduPDF

2018-03

Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa and Simon Lacoste-Julien

2018-02

A Variational Inequality Perspective on Generative Adversarial Networks
Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent and Simon Lacoste-Julien
SeaRNN: Training RNNs with Global-Local Losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin and Simon Lacoste-Julien
ICLR 2018
(2018-02-15)
hal.archives-ouvertes.frPDF
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent and Simon Lacoste-Julien
ICLR 2018
(2018-02-01)
ui.adsabs.harvard.eduPDF

2018-01

A3T: Adversarially Augmented Adversarial Training.
Akram Erraqabi, Aristide Baratin, Yoshua Bengio and Simon Lacoste-Julien
arXiv: Learning
(2018-01-12)
ui.adsabs.harvard.eduPDF
Improved asynchronous parallel optimization analysis for stochastic incremental methods
Rémi Leblond, Fabian Pedregosa and Simon Lacoste-Julien
Journal of Machine Learning Research
(2018-01-01)
jmlr.orgPDF[LATEST on arXiv preprint arXiv:1801.03749 (2018-01-11)]

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