Simon Lacoste-Julien

antonio-miguel.gois@mila.quebec

Biographie

Simon Lacoste-Julien est professeur agrégé au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal, membre cofondateur de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Il dirige également à temps partiel le SAIT AI Lab Montréal.

Ses recherches portent sur l'apprentissage automatique et les mathématiques appliquées, et intègrent des applications à la vision artificielle et au traitement du langage naturel. Il a obtenu une licence en mathématiques, physique et informatique à l’Université McGill, un doctorat en informatique à l’Université de Californie à Berkeley et un postdoctorat à l'Université de Cambridge.

Il a passé quelques années à l'Institut national de recherche en sciences et technologies du numérique (INRIA) et à l'École normale supérieure de Paris en tant que professeur de recherche avant de revenir à Montréal, en 2016, pour répondre à l'appel de Yoshua Bengio et contribuer à la croissance de l'écosystème de l'IA à Montréal.

Étudiants actuels

Alexia Jolicoeur-Martineau

Visiteur de recherche indépendant - Samsung SAIT

Doctorat - Université de Montréal

Visiteur de recherche indépendant - Samsung SAIT

Visiteur de recherche indépendant - Université de Montréal

Visiteur de recherche indépendant - Samsung SAIT

scieurda@mila.quebec

Jaewoo Lee

Visiteur de recherche indépendant - Pohang University of Science and Technology in Pohang, Korea

jaewoo.lee@mila.quebec

Jose Gallego Posada

Doctorat - Université de Montréal

Doctorat - Université de Montréal

juan.ramirez@mila.quebec

Kiho Cho

Visiteur de recherche indépendant - Samsung SAIT

kiho.cho@mila.quebec

Kwon Kisoo

Visiteur de recherche indépendant - Seoul National University, Korea

kwon.kisoo@mila.quebec

Lucas Maes

Doctorat - Université de Montréal

lucas.maes@mila.quebec

mansi.rankawat@mila.quebec

Mansi Rankawat

Doctorat - Université de Montréal

Visiteur de recherche indépendant - Samsung SAIT

marwa.el-halabi@mila.quebec

Collaborateur·rice de recherche - Université de Montréal

merajhse@mila.quebec

motahareh.sohrabi@mila.quebec

Michelle Liu

Collaborateur·rice de recherche

liumiche@mila.quebec

Motahareh Sohrabi

Maîtrise recherche - Université de Montréal

pedram.khorsandi@mila.quebec

Pedram Khorsandi

Doctorat - Université de Montréal

quentin.bertrand@mila.quebec

Quentin Bertrand

Postdoctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Gauthier Gidel

rozhin.nobahari@mila.quebec

Reza Babanezhad Harikandeh

Visiteur de recherche indépendant - Samsung SAIT

babanezr@mila.quebec

Rozhin Nobahari

Maîtrise recherche - Université de Montréal

Sébastien Lachapelle

Doctorat - Université de Montréal

lachaseb@mila.quebec

tianyue.zhang@mila.quebec

Helen Zhang

Doctorat - Université de Montréal

vitoria.barin-pacela@mila.quebec

Vitoria Barin Pacela

Doctorat - Université de Montréal

Yan Zhang

Visiteur de recherche indépendant - Samsung SAIT

yan.zhang@mila.quebec

Yash Goyal

Visiteur de recherche indépendant - Samsung SAIT

yash.goyal@mila.quebec

Décodeurs additifs pour l’identification des variables latentes et l’extrapolation du produit cartésien

Billets de blogue

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

18 mars 2024

par

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Lire l'article

Publications

PopulAtion Parameter Averaging (PAPA)

Alexia Jolicoeur-Martineau

Emy Gervais

Kilian FATRAS

Yan Zhang

2024-04-05

TMLR (accepté)

Balancing Act: Constraining Disparate Impact in Sparse Models

Meraj Hashemizadeh

Juan Ramirez

Rohan Sukumaran

Golnoosh Farnadi

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 … (voir plus)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.

2024-01-16

ICLR.cc/2024/Conference (poster)

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

2024-01-10

ArXiv (prépublication)

Weight-Sharing Regularization

Mehran Shakerinava

Motahareh Sohrabi

Siamak Ravanbakhsh

2023-11-06

ArXiv (prépublication)

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

We tackle the problems of latent variables identification and "out-of-support'' image generation in representation learning. We show that bo… (voir plus)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.

2023-09-21

NeurIPS.cc/2023/Conference (présentation orale)

Promoting Exploration in Memory-Augmented Adam using Critical Momenta

Pranshu Malviya

Goncalo Mordido

Aristide Baratin

Reza Babanezhad Harikandeh

Jerry Huang

Sarath Chandar Anbil Parthipan

Razvan Pascanu

Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (voir plus)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.

2023-07-18

ArXiv (prépublication)

On the Identifiability of Quantized Factors

Vitória Barin Pacela

Kartik Ahuja

Pascal Vincent

Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (voir plus)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.

2023-06-28

ArXiv (prépublication)

Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection

Vitória Barin-Pacela

Kartik Ahuja

Pascal Vincent

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?

Boris Knyazev

DOHA HWANG

Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communi… (voir plus)ties with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural networks. By using predicted parameters for initialization we are able to boost training of diverse ImageNet models available in PyTorch. When transferred to other datasets, models initialized with predicted parameters also converge faster and reach competitive final performance.

2023-04-24

ICML.cc/2023/Conference (poster)

CrossSplit: Mitigating Label Noise Memorization through Data Splitting

Jihye Kim

Aristide Baratin

Yan Zhang

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label cor… (voir plus)rection and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labeled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios. The project page is at https://rlawlgul.github.io/.

2023-04-24

ICML.cc/2023/Conference (poster)

A Survey of Self-Supervised and Few-Shot Object Detection

Gabriel Huang

Issam Hadj Laradji

David Vazquez

Pau Rodriguez

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (voir plus)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.

2023-04-01

IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)

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

Sébastien Lachapelle

Tristan Deleu

Divyat Mahajan

Ioannis Mitliagkas

Yoshua Bengio

Quentin Bertrand

Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding … (voir plus)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.

2023-01-01

ICML (publié)