Audrey Durand

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Associate Academic Member
Audrey Durand
Assistant Professor, Professeure adjointe, Université Laval
Audrey Durand

Audrey Durand is an Assistant Professor in the Department of Computer Science and Software Engineering and 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.



Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model.
Sophie-Camille Hogue, Flora Chen, Geneviève Brassard, Denis Lebel, Jean-François Bussières, Audrey Durand and Maxime Thibault
arXiv: Learning


Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks
Maxime Wabartha, Audrey Durand, Vincent François-Lavet and Joelle Pineau
IJCAI 2020
Deep interpretability for GWAS
Deepak Sharma, Audrey Durand, Marc-André Legault, Louis-Philippe Lemieux Perreault, Audrey Lemaçon, Marie-Pierre Dubé and Joelle Pineau
arXiv preprint arXiv:2007.01516
Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study
Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John P Kemp, Maria Nethander, Daniel Evans, John A Morris, Douglas P Kiel, Fernando Rivadeneira, Helena Johansson, Nicholas C Harvey, Dan Mellström, Magnus Karlsson, Cyrus Cooper, David M Evans, Robert Clarke, John A Kanis, Eric Orwoll... (6 more)
PLOS Medicine


Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani, Abbas Mehrabian, Audrey Durand and Branislav Kveton


A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
Nicolas Garneau, Mathieu Godbout, David Beauchemin, Audrey Durand and Luc Lamontagne


Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)
Qizhen Zhang, Audrey Durand and Joelle Pineau
AAAI 2020


Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Thang Doan, Bogdan Mazoure, Audrey Durand, Joelle Pineau and R. Devon Hjelm
arXiv: Learning


Leveraging observations in bandits: Between risks and benefits
Andrei Lupu, Audrey Durand and Doina Precup
AAAI 2019
On-line Adaptative Curriculum Learning for GANs
Thang Doan, Joao B Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau and Devon Hjelm
Leveraging exploration in off-policy algorithms via normalizing flows.
Bogdan Mazoure, Thang Doan, Audrey Durand, R. Devon Hjelm and Joelle Pineau


Temporal Regularization for Markov Decision Process


Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis
Audrey Durand, Charis Achilleos, Demetris Iacovides, Katerina Strati, Georgios D. Mitsis and Joelle Pineau
Machine Learning for Healthcare Conference
Temporal Regularization in Markov Decision Process
arXiv preprint arXiv:1811.00429


Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes
Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John A Morris, John A Kanis, Douglas P Kiel, Eugene V McCloskey, Fernando Rivadeneira, Helena Johannson, Nicholas Harvey, Cyrus Cooper, David M Evans, Joelle Pineau, William D Leslie, Celia Mt Greenwood and J Brent Richards


Leveraging Observational Learning for Exploration in Bandits
Andrei Lupu, Audrey Durand and Doina Precup
AAMAS 2018


Genomic Prediction of Osteoporosis Using 426,000 Individuals from UK Biobank
Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John Morris, John Kanis, Douglas Kiel, Eugene Mccloskey, Helena Johansson, Nicholas Harvey, Dave Evans, Joelle Pineau, William Leslie, Celia M. T. Greenwood and J. Brent Richards
Journal of Bone and Mineral Research
Streaming kernel regression with provably adaptive mean, variance, and regularization
Audrey Durand, Odalric-Ambrym Maillard and Joelle Pineau
Journal of Machine Learning Research

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