Farebrother, Jesse

20 publications

ICLR 2025 Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury

ICML 2025 Temporal Difference Flows Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Remi Munos, Alessandro Lazaric, Ahmed Touati

ICLRW 2025 Temporal Difference Flows Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Remi Munos, Alessandro Lazaric, Ahmed Touati

ICLR 2025 Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta

ICML 2024 A Distributional Analogue to the Successor Representation Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andre Barreto, Will Dabney, Marc G Bellemare, Mark Rowland

NeurIPS 2024 CALE: Continuous Arcade Learning Environment Jesse Farebrother, Pablo Samuel Castro

NeurIPS 2024 Foundations of Multivariate Distributional Reinforcement Learning Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland

ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL Johan Samir Obando Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

ICMLW 2024 Revisiting Successor Features for Inverse Reinforcement Learning Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury

ICML 2024 Stop Regressing: Training Value Functions via Classification for Scalable Deep RL Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taiga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

NeurIPSW 2024 Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta

AISTATS 2023 A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare

ICLR 2023 Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

NeurIPSW 2023 Learning Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy Max Schwarzer, Jesse Farebrother, Joshua Greaves, Kevin Roccapriore, Ekin Cubuk, Rishabh Agarwal, Aaron Courville, Marc Bellemare, Sergei Kalinin, Igor Mordatch, Pablo Castro

ICLR 2023 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPSW 2022 A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G Bellemare

NeurIPSW 2022 Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

NeurIPSW 2022 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPSW 2022 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPSW 2022 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare