Portrait of Simon Lacoste-Julien

Simon Lacoste-Julien

Core Academic Member
Canada CIFAR AI Chair
Associate Scientific Director, Mila, Associate Professor, Université de Montréal, Department of Computer Science and Operations Research
Vice President and Lab Director, Samsung Advanced Institute of Technology (SAIT) AI Lab, Montréal
Research Topics
Causality
Computer Vision
Deep Learning
Generative Models
Machine Learning Theory
Natural Language Processing
Optimization
Probabilistic Models

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

Independent visiting researcher - Samsung SAIT
Independent visiting researcher - Samsung SAIT
PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
Principal supervisor :
Independent visiting researcher - Samsung SAIT
Independent visiting researcher - Samsung SAIT
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
Independent visiting researcher - Samsung SAIT
Collaborating researcher - Université de Montréal
Independent visiting researcher - Samsung SAIT
PhD - Université de Montréal
Independent visiting researcher - Seoul National University, Korea
Independent visiting researcher - Université de Montréal
PhD - Université de Montréal
Independent visiting researcher - Pohang University of Science and Technology in Pohang, Korea
Collaborating researcher
PhD - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Collaborating researcher
Co-supervisor :
Independent visiting researcher - Samsung SAIT
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Independent visiting researcher - Samsung SAIT

Publications

Understanding Adam Requires Better Rotation Dependent Assumptions
Lucas Maes
Tianyue H. Zhang
Alexia Jolicoeur-Martineau
Damien Scieur
Charles Guille-Escuret
Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent (SGD) lacks a comprehensive theoretical explanation. This… (see more) paper investigates Adam's sensitivity to rotations of the parameter space. We demonstrate that Adam's performance in training transformers degrades under random rotations of the parameter space, indicating a crucial sensitivity to the choice of basis. This reveals that conventional rotation-invariant assumptions are insufficient to capture Adam's advantages theoretically. To better understand the rotation-dependent properties that benefit Adam, we also identify structured rotations that preserve or even enhance its empirical performance. We then examine the rotation-dependent assumptions in the literature, evaluating their adequacy in explaining Adam's behavior across various rotation types. This work highlights the need for new, rotation-dependent theoretical frameworks to fully understand Adam's empirical success in modern machine learning tasks.
Accelerating Training with Neuron Interaction and Nowcasting Networks
Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However,… (see more) learnable update rules can be costly and unstable to train and use. A simpler recently proposed approach to accelerate training is to use Adam for most of the optimization steps and periodically, only every few steps, nowcast (predict future) parameters. We improve this approach by Neuron interaction and Nowcasting (NiNo) networks. NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters by learning in a supervised way from a set of training trajectories over multiple tasks. We show that in some networks, such as Transformers, neuron connectivity is non-trivial. By accurately modeling neuron connectivity, we allow NiNo to accelerate Adam training by up to 50\% in vision and language tasks.
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
Motahareh Sohrabi
Juan Ramirez
Tianyue H. Zhang
Jose Gallego-Posada
Constrained optimization offers a powerful framework to prescribe desired behaviors in neural network models. Typically, constrained problem… (see more)s are solved via their min-max Lagrangian formulations, which exhibit unstable oscillatory dynamics when optimized using gradient descent-ascent. The adoption of constrained optimization techniques in the machine learning community is currently limited by the lack of reliable, general-purpose update schemes for the Lagrange multipliers. This paper proposes the νPI algorithm and contributes an optimization perspective on Lagrange multiplier updates based on PI controllers, extending the work of Stooke, Achiam and Abbeel (2020). We provide theoretical and empirical insights explaining the inability of momentum methods to address the shortcomings of gradient descent-ascent, and contrast this with the empirical success of our proposed νPI controller. Moreover, we prove that νPI generalizes popular momentum methods for single-objective minimization. Our experiments demonstrate that νPI reliably stabilizes the multiplier dynamics and its hyperparameters enjoy robust and predictable behavior.
Performative Prediction on Games and Mechanism Design
António Góis
Mehrnaz Mofakhami
Fernando P. Santos
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Jerry Huang
Razvan Pascanu
Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (see more)ch optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.
Weight-Sharing Regularization
Mehran Shakerinava
Motahareh Sohrabi
PopulAtion Parameter Averaging (PAPA)
Alexia Jolicoeur-Martineau
Emy Gervais
Kilian FATRAS
Yan Zhang
On the Identifiability of Quantized Factors
Vitória Barin Pacela
Kartik Ahuja
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the… (see more) theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh
Juan Ramirez
Rohan Sukumaran
Jose Gallego-Posada
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or … (see more)storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that directly addresses the disparate impact of pruning: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups.
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
Sébastien Lachapelle
Pau Rodriguez
Yash Sharma
Katie Everett
Rémi LE PRIOL
Alexandre Lacoste
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Sébastien Lachapelle
Divyat Mahajan
We tackle the problems of latent variables identification and "out-of-support'' image generation in representation learning. We show that bo… (see more)th are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
DOHA HWANG