Portrait of Audrey Durand

Audrey Durand

Associate Academic Member
audrey.durand@ift.ulaval.ca
Canada CIFAR AI Chair
Assistant Professor, Université Laval, Department of Computer Science and Software Engineering
Research Topics
Online Learning
Reinforcement Learning
Website
Google Scholar
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Biography

Audrey Durand is an assistant professor in the Department of Computer Science and Software Engineering and in the Department of Electrical and Computer Engineering at Université Laval.

She specializes in algorithms that learn through interaction with their environment using reinforcement learning, and is particularly interested in leveraging these approaches in health-related applications.

Current Students

Yohaï-Eliel Berreby
PhD - McGill University
me@yberreby.com
Website
Github
Google Scholar
Baptiste Bonin
Master's Research - Université Laval
Hugo Delhaye
Master's Research - Université de Montréal
Principal supervisor :
Lune Bellec
Mathieu Godbout
PhD - Université Laval
Github
Maxime Heuillet
PhD - Université Laval
Yasmeen Hitti
PhD - McGill University
Co-supervisor :
Gauthier Gidel
Bruno Joyal
Master's Research - Université Laval
Alexandre Larouche
PhD - Université Laval
Github
Google Scholar
Randy Lefebvre
Master's Research - Université Laval
Kaoutar Mimouni
Master's Research - Université Laval
Hadi Moazen
PhD - Université Laval

Publications

Neural Active Learning Meets the Partial Monitoring Framework
Maxime Heuillet
Ola Ahmad
Audrey Durand
We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the … (see more)costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.
2024-04-26
auai.org/UAI/2024/Conference (poster)
doi.org
openreview.net
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
Audrey Durand
2024-02-07
ArXiv (preprint)
doi.org
arxiv.org
GrowSpace: A reinforcement learning environment for plant architecture
Yasmeen Hitti
Ionelia Buzatu
Manuel Del Verme
Mark Lefsrud
Florian Golemo
Audrey Durand
2024-02-01
Computers and Electronics in Agriculture (published)
doi.org
Deep reinforcement learning for continuous wood drying production line control
François-Alexandre Tremblay
Audrey Durand
Michael Morin
Philippe Marier
Jonathan Gaudreault
2024-01-01
Computers in industry (Print) (published)
doi.org
Deep reinforcement learning for continuous wood drying production line control
François-Alexandre Tremblay
Audrey Durand
Michael Morin
Philippe Marier
Jonathan Gaudreault
2024-01-01
Computers in Industry (published)
doi.org
Knowledge by omission: the significance of omissions in the 5-choice serial reaction time task
Caroline Vouillac-Mendoza
Audrey Durand
Serge H. Ahmed
Karine Guillem
The 5-choice serial reaction time task (5-CSRTT) is commonly used to assess attention in rodents. Manipulation of this task by decreasing th… (see more)e light stimulus duration is often used to probe attentional capacity and causes a decrease in accuracy and an increase in omissions. However, although a decrease in response accuracy is commonly interpreted as a decrease in attention, it is more difficult to interpret an increase in omissions in terms of attentional performance. Here we present a series of experiments in rats that seeks to investigate the origins of these key behavioral measures of attention in the 5-CSRTT. After an initial training in the 5-CSRTT, rats were tested in a variable stimulus duration procedure to increase task difficulty and probe visual attentional capacity under several specific controlled conditions. We found that response accuracy reflects visuospatial sustained attentional processing, as commonly interpreted, while response omission reflects rats’ ignorance about the stimulus location, presumably due to failure to pay attention to the curved wall during its presentation. Moreover, when rats lack of relevant information, they choose not to respond instead of responding randomly. Overall, our results indicate that response accuracy and response omission thus correspond to two distinct attentional states.
2023-11-06
bioRxiv (preprint)
doi.org
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
Charles Bourbeau
Audrey Durand
This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that… (see more) stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions
Ellen Doney
Laurence Dion-Albert
Francois Coulombe-Rozon
Natasha Osborne
Renaud Bernatchez
Sam E.J. Paton
Fernanda Neutzling Kaufmann
Roseline Olory Agomma
José L. Solano
Raphael Gaumond
Katarzyna A. Dudek
Joanna Kasia Szyszkowicz
Manon Lebel
Alain Doyen
Audrey Durand
Flavie Lavoie-Cardinal
Marie-Claude Audet
Caroline Menard
2023-05-01
Biological Psychiatry Global Open Science (published)
doi.org
The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement
Seyedmostafa Mousavi
Dana Tannenbaum Greenberg
Ruth Ndjaboué
Michelle Greiver
Olivia Drescher
Selma Chipenda Dansokho
Denis Boutin
Jean-Marc Chouinard
Sylvie Dostie
Robert Fenton
Marley Greenberg
Jonathan McGavock
Adhiyat Najam
Monia Rekik
Tom Weisz
Donald J Willison
Audrey Durand
Holly O Witteman
2023-04-27
JMIR Diabetes (published)
doi.org
Association Rules Mining with Auto-Encoders
Th'eophile Berteloot
Richard Khoury
Audrey Durand
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to… (see more) explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there are still no efficient way association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.
2023-04-26
ArXiv (preprint)
doi.org
arxiv.org
Reducing touching eyes, nose and mouth ('T-zone') to reduce the spread of infectious disease: A prospective study of motivational, volitional and non-reflective predictors.
Mackenzie Wilson
Zachary M. van Allen
Jeremy M. Grimshaw
Jamie C. Brehaut
Audrey Durand
Jean‐François Lalonde
Douglas G. Manuel
Susan Michie
Robert West
Justin Presseau
BACKGROUND The route into the body for many pathogens is through the eyes, nose and mouth (i.e., the 'T-zone') via inhalation or fomite-base… (see more)d transfer during face touching. It is important to understand factors that are associated with touching the T-zone to inform preventive strategies. PURPOSE To identify theory-informed predictors of intention to reduce facial 'T-zone' touching and self-reported 'T-zone' touching. METHODS We conducted a nationally representative prospective questionnaire study of Canadians. Respondents were randomized to answer questions about touching their eyes, nose, or mouth with a questionnaire assessing 11 factors from an augmented Health Action Process Approach at baseline: intention, outcome expectancies, risk perception, individual severity, self-efficacy, action planning, coping planning, social support, automaticity, goal facilitation and stability of context. At 2-week follow-up, we assessed HAPA-based indicators of self-regulatory activities (awareness of standards, effort, self-monitoring) and self-reported behaviour (primary dependent variable). RESULTS Of 656 Canadian adults recruited, 569 responded to follow-up (87% response rate). Across all areas of the 'T-zone', outcome expectancy was the strongest predictor of intention to reduce facial 'T-zone' touching, while self-efficacy was a significant predictor for only the eyes and mouth. Automaticity was the strongest predictor of behaviour at the 2-week follow-up. No sociodemographic or psychological factors predicted behaviour, with the exception of self-efficacy, which negatively predicted eye touching. CONCLUSION Findings suggest that focusing on reflective processes may increase intention to reduce 'T-zone' touching, while reducing actual 'T-zone' touching may require strategies that address the automatic nature of this behaviour.
2023-03-30
British Journal of Health Psychology (published)
doi.org
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
Rupali Bhati
Jennifer Jones
Audrey Durand
Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (see more)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.
2023-02-19
ArXiv (preprint)
doi.org
arxiv.org