Theses

• Pierluca D'Oro, PhD 2024. "Building Intuitive Reinforcement Learning Algorithms"
• Evgenii Nikishin, PhD 2024. "Parameter, Experience, and Compute Efficient Deep Reinforcement Learning"
• Léa Côté-Turcotte, MSc 2024 "Domain Adaptation in Reinforcement Learning via Causal Representation Learning"
• Julien Roy, PhD 2024 "Effective Reward Specification in Deep Reinforcement Learning," in co-supervision with Chris Pal.
• Vincent Taboga, PhD 2024 "On Intelligent and Adaptive Control of Thermal Loads in Buildings under Demand Response Programs", in co-supervision with Hanane Dagdougui
• Mahan Fathi (ماهان فتحی), MSc 2024 "Beyond the Horizon: Improved Long-range Sequence Modeling from Dynamical Systems to Language"
• Sobhan Mohammadpour, MSc 2023 "On Choice Models in the Context of MDPs," in co-supervision with Emma Frejinger.
• Padideh Nouri, MSc 2023 "Sample Efficient Reinforcement Learning for Biological Sequence Design"
• David Yu-Tung Hui (許宇同), MSc 2023 "Stable Continuous Control," in co-supervision with Aaron Courville
• Paul Crouther, MSc 2023 "Model-Based Hyperparameter Optimization"
• Anushree Rankawat, MSc 2023 "Accelerated Algorithms for Temporal Difference Learning Methods"
• Simon Dufort-Labée, MSc 2021 "Steepest Descent as Linear Quadratic Regulation"
• Nikolaus Howe, MSc 2021 "Learning Neural Ordinary Differential Equations for Optimal Control"
• Michel Ma, MSc 2021 "Parsimonious Reasoning in Reinforcement Learning for Better Credit Assignment"