Durand, Audrey

23 publications

AAAI 2025 On Shallow Planning Under Partial Observability Randy Lefebvre, Audrey Durand

UAI 2024 Neural Active Learning Meets the Partial Monitoring Framework Maxime Heuillet, Ola Ahmad, Audrey Durand

ICML 2024 Randomized Confidence Bounds for Stochastic Partial Monitoring Maxime Heuillet, Ola Ahmad, Audrey Durand

ICMLW 2024 Randomized Confidence Bounds for Stochastic Partial Monitoring Maxime Heuillet, Ola Ahmad, Audrey Durand

JMLR 2023 Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm Louis-Philippe Vignault, Audrey Durand, Pascal Germain

AAAI 2023 Latent Space Evolution Under Incremental Learning with Concept Drift (Student Abstract) Charles Bourbeau, Audrey Durand

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

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

NeurIPSW 2022 Performative Prediction in Time Series: A Case Study Rupali Bhati, Jennifer Jones, David Langelier, Anthony Reiman, Jonathan Greenland, Kristin Campbell, Audrey Durand

NeurIPSW 2022 Tracking the Risk of Machine Learning Systems with Partial Monitoring Maxime Heuillet, Audrey Durand

ECML-PKDD 2021 Routine Bandits: Minimizing Regret on Recurring Problems Hassan Saber, Léo Saci, Odalric-Ambrym Maillard, Audrey Durand

ICMLW 2021 Sequential Automated Machine Learning: Bandits-Driven Exploration Using a Collaborative Filtering Representation Maxime Heuillet, Benoit Debaque, Audrey Durand

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

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

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

CoRL 2019 Leveraging Exploration in Off-Policy Algorithms via Normalizing Flows Bogdan Mazoure, Thang Doan, 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 On-Line Adaptative Curriculum Learning for GANs Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm

MLHC 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

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

JMLR 2018 Streaming Kernel Regression with Provably Adaptive Mean, Variance, and Regularization Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau

NeurIPS 2018 Temporal Regularization for Markov Decision Process Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup