SCHNAPS: a generic population-based simulator for public health purposes. - Audrey Durand; Christian Gagné 001; Marc-André Gardner; François Rousseau; Yves Giguère; Daniel Reinharz (SummerSim, 2010)

Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space. - Julien-Charles Levesque; Audrey Durand; Christian Gagné 001; Robert Sabourin (GECCO, 2012)

Population-Based Simulation for Public Health: Generic Software Infrastructure and Its Application to Osteoporosis. - Audrey Durand; Christian Gagné 001; Léon Nshimyumukiza; Mathieu Gagnon; François Rousseau; Yves Giguère; Daniel Reinharz (IEEE Trans. Syst. Man Cybern. Part A, 2012)

Bayesian classification and unsupervised learning for isolating weeds in row crops. - François-Michel De Rainville; Audrey Durand; Félix-Antoine Fortin; Kevin Tanguy; Xavier Maldague; Bernard Panneton; Marie-Josée Simard (Pattern Anal. Appl., 2014)

Adaptive Treatment Allocation Using Sub-Sampled Gaussian Processes. - Audrey Durand; Joelle Pineau (AAAI Fall Symposia, 2015)

Bayesian optimization for conditional hyperparameter spaces. - Julien-Charles Levesque; Audrey Durand; Christian Gagné 001; Robert Sabourin (IJCNN, 2017)

Estimating Quality in User-Guided Multi-Objective Bandits Optimization. - Audrey Durand; Christian Gagné 001 (CoRR, 2017)

Query Completion Using Bandits for Engines Aggregation. - Audrey Durand; Jean-Alexandre Beaumont; Christian Gagné 001; Michel Lemay; Sébastien Paquet (CoRR, 2017)

Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy. - Louis-Émile Robitaille; Audrey Durand; Marc-André Gardner; Christian Gagné 001; Paul De Koninck; Flavie Lavoie-Cardinal (AAAI, 2018)

Rating Super-Resolution Microscopy Images With Deep Learning. - Louis-Émile Robitaille; Audrey Durand; Marc-André Gardner; Christian Gagné 001; Paul De Koninck; Flavie Lavoie-Cardinal (AAAI, 2018)

Leveraging Observational Learning for Exploration in Bandits. - Andrei Lupu; Audrey Durand; Doina Precup (AAMAS, 2018)

Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis. - Audrey Durand; Charis Achilleos; Demetris Iacovides; Katerina Strati; Georgios D. Mitsis; Joelle Pineau (MLHC, 2018)

Temporal Regularization for Markov Decision Process. - Pierre Thodoroff; Audrey Durand; Joelle Pineau; Doina Precup (NeurIPS, 2018)

Online Adaptative Curriculum Learning for GANs. - Thang Doan; João Monteiro 002; Isabela Albuquerque; Bogdan Mazoure; Audrey Durand; Joelle Pineau; R. Devon Hjelm (CoRR, 2018)

Streaming kernel regression with provably adaptive mean, variance, and regularization. - Audrey Durand; Odalric-Ambrym Maillard; Joelle Pineau (J. Mach. Learn. Res., 2018)

On-Line Adaptative Curriculum Learning for GANs. - Thang Doan; João Monteiro 002; Isabela Albuquerque; Bogdan Mazoure; Audrey Durand; Joelle Pineau; R. Devon Hjelm (AAAI, 2019)

Leveraging Observations in Bandits: Between Risks and Benefits. - Andrei Lupu; Audrey Durand; Doina Precup (AAAI, 2019)

Leveraging exploration in off-policy algorithms via normalizing flows. - Bogdan Mazoure; Thang Doan; Audrey Durand; Joelle Pineau; R. Devon Hjelm (CoRL, 2019)

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning. - Thang Doan; Bogdan Mazoure; Audrey Durand; Joelle Pineau; R. Devon Hjelm (CoRR, 2019)

Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract). - Qizhen Zhang 002; Audrey Durand; Joelle Pineau (AAAI, 2020)

Old Dog Learns New Tricks: Randomized UCB for Bandit Problems. - Sharan Vaswani; Abbas Mehrabian; Audrey Durand; Branislav Kveton (AISTATS, 2020)

Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks. - Maxime Wabartha; Audrey Durand; Vincent François-Lavet; Joelle Pineau (IJCAI, 2020)

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; Luc Lamontagne (LREC, 2020)

Deep interpretability for GWAS. - Deepak Sharma; Audrey Durand; Marc-André Legault; Louis-Philippe Lemieux Perreault; Audrey Lemaçon; Marie-Pierre Dubé; Joelle Pineau (CoRR, 2020)

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; Maxime Thibault (CoRR, 2020)

Routine Bandits: Minimizing Regret on Recurring Problems. - Hassan Saber; Léo Saci; Odalric-Ambrym Maillard; Audrey Durand (ECML/PKDD (1), 2021)

Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments. - Joseph Jay Williams; Jacob Nogas; Nina Deliu; Hammad Shaikh; Sofia S. Villar; Audrey Durand; Anna N. Rafferty (CoRR, 2021)

CARL: Conditional-value-at-risk Adversarial Reinforcement Learning. - Mathieu Godbout; Maxime Heuillet; Sharath Chandra; Rupali Bhati; Audrey Durand (CoRR, 2021)

GrowSpace: Learning How to Shape Plants. - Yasmeen Hitti; Ionelia Buzatu; Manuel Del Verme; Mark Lefsrud; Florian Golemo; Audrey Durand (CoRR, 2021)

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; Audrey Durand; Maxime Thibault (J. Am. Medical Informatics Assoc., 2021)

Exploring polypharmacy with artificial intelligence: data analysis protocol. - Caroline Sirois; Richard Khoury; Audrey Durand; Pierre-Luc Déziel; Olga Bukhtiyarova; Yohann Chiu; Denis Talbot; Alexandre Bureau; Philippe Després; Christian Gagné 001; François Laviolette; Anne-Marie Savard; et al. (BMC Medical Informatics Decis. Mak., 2021)

Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract). - Renaud Bernatchez; Audrey Durand; Flavie Lavoie-Cardinal (AAAI, 2022)

A Game-Theoretic Perspective on Risk-Sensitive Reinforcement Learning. - Mathieu Godbout; Maxime Heuillet; Sharath Chandra Raparthy; Rupali Bhati; Audrey Durand (SafeAI@AAAI, 2022)

Contextual bandit optimization of super-resolution microscopy. - Anthony Bilodeau; Renaud Bernatchez; Albert Michaud-Gagnon; Flavie Lavoie-Cardinal; Audrey Durand (Canadian AI, 2022)

Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining. - Théophile Berteloot; Richard Khoury; Audrey Durand (CoRR, 2022)

Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy. - Alexandre Larouche; Audrey Durand; Richard Khoury; Caroline Sirois (CoRR, 2022)

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; Audrey Durand; Flavie Lavoie-Cardinal (Nat. Mach. Intell., 2022)

Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract). - Charles Bourbeau; Audrey Durand (AAAI, 2023)

Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients. - Rupali Bhati; Jennifer Jones; Audrey Durand (CoRR, 2023)

Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. - Louis-Philippe Vignault; Audrey Durand; Pascal Germain (J. Mach. Learn. Res., 2023)

Randomized Confidence Bounds for Stochastic Partial Monitoring. - Maxime Heuillet; Ola Ahmad; Audrey Durand (ICML, 2024)

Association Rules Mining with Auto-encoders. - Théophile Berteloot; Richard Khoury; Audrey Durand (IDEAL (1), 2024)

Data harmonization for Advancing research on Personalized Rehabilitation Interventions for Patients with Traumatic Brain Injury and Stroke: A proof of concept. - Dorra Rakia Allegue; Despoina Petsani; Nathalie Ponthon; Evdokimos I. Konstantinidis; Panagiotis D. Bamidis; Eva Kehayia; Audrey Durand; Sara Ahmed (PETRA, 2024)

Neural Active Learning Meets the Partial Monitoring Framework. - Maxime Heuillet; Ola Ahmad; Audrey Durand (UAI, 2024)

GrowSpace: A reinforcement learning environment for plant architecture. - Yasmeen Hitti; Ionelia Buzatu; Manuel Del Verme; Mark Lefsrud; Florian Golemo; Audrey Durand (Comput. Electron. Agric., 2024)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED. - Anthony Bilodeau; Albert Michaud-Gagnon; Julia Chabbert; Benoît Turcotte; Jörn Heine; Audrey Durand; Flavie Lavoie-Cardinal (Nat. Mac. Intell., 2024)

Deep reinforcement learning for continuous wood drying production line control. - François-Alexandre Tremblay; Audrey Durand; Michael Morin; Philippe Marier; Jonathan Gaudreault (Comput. Ind., 2024)

On Shallow Planning Under Partial Observability. - Randy Lefebvre; Audrey Durand (AAAI, 2025)

Platform-based Adaptive Experimental Research in Education: Lessons Learned from The Digital Learning Challenge. - Ilya Musabirov; Mohi Reza; Haochen Song; Steven Moore; Pan Chen; Harsh Kumar; Tong Li; John C. Stamper; Norman L. Bier; Anna N. Rafferty; Thomas W. Price; Nina Deliu; et al. (LAK, 2025)

WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings. - Haochen Song; Ilya Musabirov; Ananya Bhattacharjee; Audrey Durand; Meredith Franklin; Anna N. Rafferty; Joseph Jay Williams (CoRR, 2025)

Preference-based learning for news headline recommendation. - Alexandre Bouras; Audrey Durand; Richard Khoury (CoRR, 2025)

On the Fundamental Limitations of Dual Static CVaR Decompositions in Markov Decision Processes. - Mathieu Godbout; Audrey Durand (CoRR, 2025)

Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts. - Maxime Heuillet; Yufei Cui; Boxing Chen; Audrey Durand; Prasanna Parthasarathi (CoRR, 2025)