Christian Gagné

Biography

Christian Gagné has been a professor in the Department of Electrical and Computer Engineering at Université Laval since 2008.

He is the director of the Institute Intelligence and Data (IID), holds a Canada CIFAR AI Chair, and is an associate member of Mila – Quebec Artificial Intelligence Institute.

Gagné is also a member of Université Laval’s Computer Vision and Systems Laboratory (LVSN), as well as its Robotics, Vision and Machine Intelligence Research Centre (CeRVIM) and its Big Data Research Centre (CRDM). He is a member of the REPARTI and UNIQUE strategic clusters of the FRQNT, the VITAM centre of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technologies (OBVIA).

Gagné’s research focuses on the development of methods for machine learning and stochastic optimization. In particular, he is interested in deep neural networks, representation learning and transfer, meta-learning and multitasking. He is also interested in optimization approaches based on probabilistic models and evolutionary algorithms, including black-box optimization and automatic programming. An important part of his work is the practical application of these techniques in fields like computer vision, microscopy, healthcare, energy and transportation.

Current Students

Frédéric Beaupré

PhD - Université Laval

Catherine Bouchard

PhD - Université Laval

Olivier Bussière

Master's Research - Université Laval

Sara Karami

PhD - Université Laval

Rose Nayoung Kwon

PhD - Université Laval

Benjamin Léger

PhD - Université Laval

Samy Mammeri

Undergraduate - Université Laval

Jonas Ngnawe

PhD - Université Laval

PhD - Université Laval

Website

Adam Tupper

PhD - Université Laval

Website

Publications

Evolving Domain Generalization

Wei Wang

Gezheng Xu

Ruizhi Pu

Jiaqi Li

Fan Zhou

Changjian Shui

Charles Ling

Boyu Wang

2022-05-30

ArXiv (preprint)

arxiv.org

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients

Daniel Gourdeau

Olivier Potvin

Jason Henry Biem

Florence Cloutier

Lyna Abrougui

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Louis Gagnon

Raphaelle Giguère

Alexandre Hains

Huy Le

Simon Lemieux

Marie-Hélène Lévesque

Simon Nepveu

Lorne Rosenbloom

An Tang

Issac Yang

Nathalie Duchesne … (see 1 more)

Simon Duchesne

2022-04-12

Scientific Reports (published)

Active Learning for Capturing Human Decision Policies in a Data Frugal Context

Loïc Grossetête

Alexandre Marois

Bénédicte Chatelais

Daniel Lafond

2022-02-01

Machine Learning, Optimization, and Data Science (published)

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Tal Arbel

Boyu Wang

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)

openreview.net

On the benefits of representation regularization in invariance based domain generalization

Changjian Shui

Boyu Wang

A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Dom… (see more)ain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.

2021-12-31

Machine Learning (published)

arxiv.org

Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons

Flavie Lavoie-Cardinal

Anthony Bilodeau

Mado Lemieux

Marc-André Gardner

Theresa Wiesner

Gabrielle Laramée