Audrey Durand

julien.armand@mila.quebec

Biographie

Audrey Durand est professeure adjointe au Département d’informatique et de génie logiciel ainsi qu’au Département de génie électrique et de génie informatique de l’Université Laval. Elle se spécialise dans les algorithmes qui apprennent par l’interaction avec leur environnement, soit l’apprentissage par renforcement, et s’intéresse particulièrement à l’application de ces approches au domaine de la santé.

Étudiants actuels

Julien Armand

Maîtrise recherche - Université Laval

Doctorat - McGill

Maîtrise recherche - Université Laval

baptiste.bonin@mila.quebec

Hugo Delhaye

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Lune Bellec

hugo.delhaye@mila.quebec

Erinn Grigsby

Postdoctorat - UdeM

erinn.grigsby@mila.quebec

Site web

Github

alexandre.larouche@mila.quebec

Bruno Joyal

Maîtrise recherche - Université Laval

bruno.joyal@mila.quebec

Alexandre Larouche

Doctorat - Université Laval

Github

randy.lefebvre@mila.quebec

Randy Lefebvre

Doctorat - Université Laval

Hadi Moazen

Doctorat - Université Laval

hadi.moazen@mila.quebec

Caro Strickland

Postdoctorat - Université Laval

caro.strickland@mila.quebec

Site web

Github

Publications

Contextual bandit optimization of super-resolution microscopy

Anthony Bilodeau

Renaud Bernatchez

Albert Michaud-Gagnon

Flavie Lavoie-Cardinal

2022-05-26

Applied Informatics (publié)

Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations

Anthony Bilodeau

Constantin V.L. Delmas

Martin Parent

Paul De Koninck

Flavie Lavoie-Cardinal

2022-04-13

Nature Machine Intelligence (publié)

Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features

Alexandre Lachance

Mathieu Godbout

Fares Antaki

Mélanie Hébert

Serge Bourgault

Mathieu Caissie

Éric Tourville

Ali Dirani

2022-04-05

Translational Vision Science & Technology (publié)

Biomedical Research & Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for a longitudinal evaluation of mobility outcomes (Preprint)

Sara Ahmed

Philippe Archambault

Claudine Auger

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

Bonnie Swaine

2022-01-12

(publié)

GrowSpace: Learning How to Shape Plants

Mark Lefsrud

Plants are dynamic systems that are integral to our existence and survival. Plants face environment changes and adapt over time to their sur… (voir plus)rounding conditions. We argue that plant responses to an environmental stimulus are a good example of a real-world problem that can be approached within a reinforcement learning (RL)framework. With the objective of controlling a plant by moving the light source, we propose GrowSpace, as a new RL benchmark. The back-end of the simulator is implemented using the Space Colonisation Algorithm, a plant growing model based on competition for space. Compared to video game RL environments, this simulator addresses a real-world problem and serves as a test bed to visualize plant growth and movement in a faster way than physical experiments. GrowSpace is composed of a suite of challenges that tackle several problems such as control, multi-stage learning,fairness and multi-objective learning. We provide agent baselines alongside case studies to demonstrate the difficulty of the proposed benchmark.

2021-12-31

AIAFS @ AAAI Conference on Artificial Intelligence (publié)

openreview.net

Routine Bandits: Minimizing Regret on Recurring Problems

Hassan Saber

L'eo Saci

Odalric-Ambrym Maillard

2021-09-09

Machine Learning and Knowledge Discovery in Databases. Research Track (publié)

Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders

Sophie-Camille Hogue

Flora Chen

Geneviève Brassard

Denis Lebel

Jean-François Bussières

Maxime Thibault

2021-05-05

J. Am. Medical Informatics Assoc. (publié)

Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks

Maxime Wabartha

Vincent François-Lavet

By virtue of their expressive power, neural networks (NNs) are well suited to fitting large, complex datasets, yet they are also known to … (voir plus)produce similar predictions for points outside the training distribution. As such, they are, like humans, under the influence of the Black Swan theory: models tend to be extremely "surprised" by rare events, leading to potentially disastrous consequences, while justifying these same events in hindsight. To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating to novel data points. This leads DENN to output highly uncertain predictions for unexpected inputs. We achieve this by adding a diversity term in the loss function used to train the model, computed at specific inputs. We first illustrate the usefulness of the method on a low-dimensional regression problem. Then, we show how the loss can be adapted to tackle anomaly detection during classification, as well as safe imitation learning problems.

2020-07-10

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (publié)

Deep interpretability for GWAS

Deepak Sharma

Marc-Andr Legault

Louis-Philippe Lemieux Perreault

Audrey Lemaon

Marie-Pierre Dub

Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In the… (voir plus)se studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.

2020-07-02

arXiv (Cornell University) (prépublication)

arxiv.org

Old Dog Learns New Tricks: Randomized UCB for Bandit Problems

Sharan Vaswani

Abbas Mehrabian

Branislav Kveton

2020-06-02

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

proceedings.mlr.press

Leveraging exploration in off-policy algorithms via normalizing flows

Bogdan Mazoure

Thang Doan

R Devon Hjelm

The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in … (voir plus)many real-world scenarios. Approaches such as neural density models and continuous exploration (e.g., Go-Explore) have been proposed to maintain the high exploration rate necessary to find high performing and generalizable policies. Soft actor-critic(SAC) is another method for improving exploration that aims to combine efficient learning via off-policy updates while maximizing the policy entropy. In this work, we extend SAC to a richer class of probability distributions (e.g., multimodal) through normalizing flows (NF) and show that this significantly improves performance by accelerating the discovery of good policies while using much smaller policy representations. Our approach, which we call SAC-NF, is a simple, efficient,easy-to-implement modification and improvement to SAC on continuous control baselines such as MuJoCo and PyBullet Roboschool domains. Finally, SAC-NF does this while being significantly parameter efficient, using as few as 5.5% the parameters for an equivalent SAC model.

2020-05-11

Proceedings of the Conference on Robot Learning (publié)

proceedings.mlr.press

Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)

Qizhen Zhang

2020-04-02

AAAI Conference on Artificial Intelligence (publié)