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

olivier2202@gmail.com

Cynthia García Ybarra

Master's Research - Université Laval

Sara Karami

PhD - Université Laval

Benjamin Léger

PhD - Université Laval

Samy Mammeri

Research Intern - Université Laval

Jonas Ngnawe

PhD - Université Laval

Fatemeh Nourilenjan Nokabadi

PhD - Université Laval

PhD - Université Laval

Adam Tupper

PhD - Université Laval

adam.tupper.1@ulaval.ca

Publications

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

Loïc Grossetête

Alexandre Marois

Bénédicte Chatelais

Daniel Lafond

2022-02-02

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.

openreview.net

On the benefits of representation regularization in invariance based domain generalization

Changjian Shui

Boyu Wang

2022-01-01

Machine Learning (published)

On the benefits of representation regularization in invariance based domain generalization

Changjian Shui

Boyu Wang

2021-05-30

ArXiv (preprint)

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

Loïc Grossetête

Alexandre Marois

Bénédicte Chatelais

Daniel Lafond

2021-01-01

International Conference on Machine Learning, Optimization, and Data Science (published)

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

Paul De Koninck

2020-07-20

Scientific Reports (published)

Deep Active Learning: Unified and Principled Method for Query and Training

Changjian Shui

Fan Zhou

Boyu Wang

2020-06-03

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

proceedings.mlr.press

Deep Active Learning: Unified and Principled Method for Query and Training

Changjian Shui

Fan Zhou

Boyu Wang

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. … (see more)We are providing theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting the Wasserstein distance. As a consequence, we derived a new training loss from the theoretical analysis, which is decomposed into optimizing deep neural network parameters and batch query selection through alternative optimization. In addition, the loss for training a deep neural network is naturally formulated as a min-max optimization problem through leveraging the unlabeled data information. Moreover, the proposed principles also indicate an explicit uncertainty-diversity trade-off in the query batch selection. Finally, we evaluate our proposed method on different benchmarks, consistently showing better empirical performances and a better time-efficient query strategy compared to the baselines.

2019-11-20

ArXiv (preprint)