Simon Lacoste-Julien

aristidebaratin@hotmail.com

Biography

Simon Lacoste-Julien is an associate professor at Mila – Quebec Artificial Intelligence Institute and in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Canada CIFAR AI Chair and heads (part time) the SAIT AI Lab Montréal.

Lacoste-Julien‘s research interests are machine learning and applied mathematics, along with their applications to computer vision and natural language processing. He completed a BSc in mathematics, physics and computer science at McGill University, a PhD in computer science at UC Berkeley and a postdoc at the University of Cambridge.

After spending several years as a researcher at INRIA and the École normale supérieure in Paris, he returned to his home city of Montréal in 2016 to answer Yoshua Bengio’s call to help grow the Montréal AI ecosystem.

Current Students

Reza Babanezhad Harikandeh

Independent visiting researcher - Samsung SAIT

Aristide Baratin

Independent visiting researcher - Samsung SAIT

PhD - Université de Montréal

Collaborating Alumni - Université de Montréal

Principal supervisor :

Independent visiting researcher - Samsung SAIT

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

Yash Goyal

Independent visiting researcher - Samsung SAIT

yashgoyal.yg1@gmail.com

Meraj Hashemizadeh

Collaborating researcher - Université de Montréal

Alexia Jolicoeur-Martineau

Independent visiting researcher - Samsung SAIT

PhD - Université de Montréal

Kwon Kisoo

Independent visiting researcher - Seoul National University, Korea

Boris Knyazev

Independent visiting researcher - Université de Montréal

PhD - Université de Montréal

Jaewoo Lee

Independent visiting researcher - Pohang University of Science and Technology in Pohang, Korea

Lucas Maes

PhD - Université de Montréal

Master's Research - Université de Montréal

Juan Ramirez

PhD - Université de Montréal

PhD - Université de Montréal

Theo Saulus

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Damien Scieur

Independent visiting researcher - Samsung SAIT

Motahareh Sohrabi

Collaborating Alumni - Université de Montréal

Helen Zhang

PhD - Université de Montréal

Yan Zhang

Independent visiting researcher - Samsung SAIT

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

Blog Posts

March 18, 2024

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Read the article

Publications

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.

Waiss Azizian

2020-01-01

International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games

Waiss Azizian

We consider diﬀerentiable games where the goal is to ﬁnd a Nash equilibrium. The machine learning community has recently started using v… (see more)ariants of the gradient method ( GD ). Prime examples are extragradient ( EG ), the optimistic gradient method ( OG ) and consensus optimization ( CO ), which enjoy linear convergence in cases like bilinear games, where the standard GD fails. The full bene-ﬁts of theses relatively new methods are not known as there is no uniﬁed analysis for both strongly monotone and bilinear games. We provide new analyses of the EG ’s local and global convergence properties and use is to get a tighter global convergence rate for OG and CO . Our analysis covers the whole range of settings between bilinear and strongly monotone games. It reveals that these methods converges via diﬀerent mechanisms at these extremes; in between, it exploits the most favorable mechanism for the given problem. We then prove that EG achieves the optimal rate for a wide class of algorithms with any number of extrapolations. Our tight analysis of EG ’s convergence rate in games shows that, unlike in convex minimization, EG may be much faster than GD .

2020-01-01

International Conference on Artificial Intelligence and Statistics (published)

dblp.uni-trier.de

Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation

S. Meng

Sharan Vaswani

Issam Hadj Laradji

Mark Schmidt

We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied b… (see more)y over-parameterized models. Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size. By growing the batch size for both the subsampled gradient and Hessian, we show that R-SSN can converge at a quadratic rate in a local neighbourhood of the solution. We also show that R-SSN attains local linear convergence for the family of self-concordant functions. Furthermore, we analyze stochastic BFGS algorithms in the interpolation setting and prove their global linear convergence. We empirically evaluate stochastic L-BFGS and a "Hessian-free" implementation of R-SSN for binary classification on synthetic, linearly-separable datasets and real datasets under a kernel mapping. Our experimental results demonstrate the fast convergence of these methods, both in terms of the number of iterations and wall-clock time.

2019-10-11

ArXiv (preprint)

GAIT: A Geometric Approach to Information Theory

Jose Gallego-Posada

Ankit Vani

Max Schwarzer

We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities … (see more)between them. This concept was originally introduced in theoretical ecology to study the diversity of ecosystems. Based on this notion of entropy, we introduce geometry-aware counterparts for several concepts and theorems in information theory. Notably, our proposed divergence exhibits performance on par with state-of-the-art methods based on the Wasserstein distance, but enjoys a closed-form expression that can be computed efficiently. We demonstrate the versatility of our method via experiments on a broad range of domains: training generative models, computing image barycenters, approximating empirical measures and counting modes.

2019-06-19

ArXiv (preprint)

Negative Momentum for Improved Game Dynamics

Reyhane Askari Hemmat

Mohammad Pezeshki

Gabriel Huang

Rémi LE PRIOL

Games generalize the single-objective optimization paradigm by introducing different objective functions for different players. Differentiab… (see more)le games often proceed by simultaneous or alternating gradient updates. In machine learning, games are gaining new importance through formulations like generative adversarial networks (GANs) and actor-critic systems. However, compared to single-objective optimization, game dynamics are more complex and less understood. In this paper, we analyze gradient-based methods with momentum on simple games. We prove that alternating updates are more stable than simultaneous updates. Next, we show both theoretically and empirically that alternating gradient updates with a negative momentum term achieves convergence in a difficult toy adversarial problem, but also on the notoriously difficult to train saturating GANs.

2019-04-11

Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning

Eric Larsen

Sébastien Lachapelle

Yoshua Bengio

Emma Frejinger

Andrea Lodi

The paper provides a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a metho… (see more)dology to quickly predict solution summaries (i.e., solution descriptions at a given level of detail) to discrete stochastic optimization problems. We approximate the solutions based on supervised learning and the training dataset consists of a large number of deterministic problems that have been solved independently and offline. Uncertainty regarding a missing subset of the inputs is addressed through sampling and aggregation methods. Our motivating application concerns booking decisions of intermodal containers on double-stack trains. Under perfect information, this is the so-called load planning problem and it can be formulated by means of integer linear programming. However, the formulation cannot be used for the application at hand because of the restricted computational budget and unknown container weights. The results show that standard deep learning algorithms allow one to predict descriptions of solutions with high accuracy in very short time (milliseconds or less).

2018-07-31

arXiv.org (preprint)

dblp.uni-trier.de

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

Eric P. Larsen

Sébastien Lachapelle

Yoshua Bengio

Emma Frejinger

Andrea Lodi

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a method… (see more)ology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming, where the second stage is demanding computationally. We aim to predict at a high speed the expected TDOS associated with the second-stage problem, conditionally on the first-stage variables. This may be used in support of the solution to the overall two-stage problem by avoiding the online generation of multiple second-stage scenarios and solutions. We formulate the tactical prediction problem as a stochastic optimal prediction program, whose solution we approximate with supervised machine learning. The training data set consists of a large number of deterministic operational problems generated by controlled probabilistic sampling. The labels are computed based on solutions to these problems (solved independently and offline), employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application on load planning for rail transportation show that deep learning models produce accurate predictions in very short computing time (milliseconds or less). The predictive accuracy is close to the lower bounds calculated based on sample average approximation of the stochastic prediction programs.

2018-07-31

ArXiv (preprint)

doi.org

Negative Momentum for Improved Game Dynamics

Reyhane Askari Hemmat

Mohammad Pezeshki

Gabriel Huang

Rémi LE PRIOL

2018-07-12

ArXiv (preprint)

Frank-Wolfe Splitting via Augmented Lagrangian Method

Fabian Pedregosa

Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than mini… (see more)mizing it over each individual constraint set. While traditional methods such as Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the intersection, splitting techniques take advantage of the structure of each sets, and only require access to the oracle on the individual constraints. In this work, we develop and analyze the Frank-Wolfe Augmented Lagrangian (FW-AL) algorithm, a method for minimizing a smooth function over convex compact sets related by a "linear consistency" constraint that only requires access to a linear minimization oracle over the individual constraints. It is based on the Augmented Lagrangian Method (ALM), also known as Method of Multipliers, but unlike most existing splitting methods, it only requires access to linear (instead of quadratic) minimization oracles. We use recent advances in the analysis of Frank-Wolfe and the alternating direction method of multipliers algorithms to prove a sublinear convergence rate for FW-AL over general convex compact sets and a linear convergence rate for polytopes.

2018-03-31

ArXiv (preprint)

Frank-Wolfe Splitting via Augmented Lagrangian Method

Fabian Pedregosa

2018-01-01

AISTATS (published)

proceedings.mlr.press