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

babanezr@mila.quebec

Aristide Baratin

Independent visiting researcher - Samsung SAIT

PhD - Université de Montréal

vitoria.barin-pacela@mila.quebec

PhD - Université de Montréal

simon.dufort-labbe@mila.quebec

Marwa El Halabi

Independent visiting researcher - Samsung SAIT

marwa.el-halabi@mila.quebec

PhD - Université de Montréal

antonio-miguel.gois@mila.quebec

Yash Goyal

Independent visiting researcher - Samsung SAIT

yashgoyal.yg1@gmail.com

Meraj Hashemizadeh

Collaborating researcher - Université de Montréal

merajhse@mila.quebec

fahimeh.hosseini@mila.quebec

Fahimeh HosseiniNoohdani

Collaborating researcher - Université de Montréal

pedram.khorsandi@mila.quebec

Pedram Khorsandi

PhD - Université de Montréal

Boris Knyazev

Independent visiting researcher - Université de Montréal

Independent visiting researcher - Samsung - SAIT

lachaseb@mila.quebec

yukti.makhija@mila.quebec

Lucas Maes

PhD - Université de Montréal

lucas.maes@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Ioannis Mitliagkas

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

alan.milligan@mila.quebec

Collaborating Alumni - Université de Montréal

rozhin.nobahari@mila.quebec

Juan Ramirez

PhD - Université de Montréal

juan.ramirez@mila.quebec

PhD - Université de Montréal

mansi.rankawat@mila.quebec

Independent visiting researcher - Univeristy of Tübingen

Theo Saulus

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

theo.saulus@mila.quebec

motahareh.sohrabi@mila.quebec

Damien Scieur

Independent visiting researcher - Samsung SAIT

Collaborating researcher - Université de Montréal

tianyue.zhang@mila.quebec

Tianyue (Helen) Zhang

PhD - Université de Montréal

Yan Zhang

Independent visiting researcher - Samsung SAIT

yan.zhang@mila.quebec

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

Quantized Disentanglement: A Practical Approach

Vitória Barin-Pacela

Kartik Ahuja

P Vincent

2025-06-08

ICML.cc/2025/Workshop/SIM (poster)

Performative Prediction on Games and Mechanism Design

Fernando P. Santos

2025-04-22

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (published)

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. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.

2025-01-21

ICLR.cc/2025/Conference (poster)

Feasible Learning

Juan Ramirez

Ignacio Hounie

Juan Elenter

Jose Gallego-Posada

Meraj Hashemizadeh

Alejandro Ribeiro

We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bound… (see more)s the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance \emph{on every individual data point}. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.

2025-01-21

aistats.org/AISTATS/2025/Conference (poster)

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

2024-07-22

International Conference on Machine Learning (Accept (Poster))

Reza Babanezhad Harikandeh

Promoting Exploration in Memory-Augmented Adam using Critical Momenta

Pranshu Malviya

Goncalo Mordido

Aristide Baratin

Adaptive gradient-based optimizers, notably Adam, have left their mark in training large-scale deep learning models, offering fast convergen… (see more)ce and robustness to hyperparameter settings. However, they often struggle with generalization, attributed to their tendency to converge to sharp minima in the loss landscape. To address this, we propose a new memory-augmented version of Adam that encourages exploration towards flatter minima by incorporating a buffer of critical momentum terms during training. This buffer prompts the optimizer to overshoot beyond narrow minima, promoting exploration. Through comprehensive analysis in simple settings, we illustrate the efficacy of our approach in increasing exploration and bias towards flatter minima. We empirically demonstrate that it can improve model performance for image classification on ImageNet and CIFAR10/100, language modelling on Penn Treebank, and online learning tasks on TinyImageNet and 5-dataset. Our code is available at https://github.com/chandar-lab/CMOptimizer.

2024-06-08

Transactions on Machine Learning Research (accepted)

Weight-Sharing Regularization

Weight-sharing is ubiquitous in deep learning. Motivated by this, we propose a "weight-sharing regularization" penalty on the weights …

2024-04-17

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (published)

Alexia Jolicoeur-Martineau

PopulAtion Parameter Averaging (PAPA)

Emy Gervais

Kilian Fatras

Yang Zhang

2024-04-04

TMLR (accepted)

Balancing Act: Constraining Disparate Impact in Sparse Models

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.

2024-01-15

ICLR.cc/2024/Conference (poster)

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

Sébastien Lachapelle

Pau Rodríguez

Yash Sharma

Katie Everett

Rémi Le Priol

Alexandre Lacoste

2024-01-09

ArXiv (preprint)

arxiv.org

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

We tackle the problems of latent variables identification and ``out-of-support'' image generation in representation learning. We show that b… (see more)oth 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.

2023-12-11

Neural Information Processing Systems (Accept (oral))

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding … (see more)is limited. In this work, we provide evidence that disentangled representations coupled with sparse base-predictors improve generalization. In the context of multi-task learning, we prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem. Finally, we explore a meta-learning version of this algorithm based on group Lasso multiclass SVM base-predictors, for which we derive a tractable dual formulation. It obtains competitive results on standard few-shot classification benchmarks, while each task is using only a fraction of the learned representations.

2023-07-24

International Conference on Machine Learning (Accept (Poster))