Jesse Farebrother

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Marlos C. MachadoUniversity of Alberta; AmiiVerified email at ualberta.ca
Rishabh AgarwalSenior Research Scientist, Google DeepMindVerified email at google.com
Marc G. BellemareReliant AIVerified email at reliant.ai
Pablo Samuel CastroGoogleVerified email at google.com
Aaron CourvilleProfessor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
Michael BowlingAmii, University of AlbertaVerified email at ualberta.ca
Adrien Ali TaïgaUniversité de MontréalVerified email at umontreal.ca
Mark RowlandResearch Scientist, Google DeepMindVerified email at google.com
Charline Le LanGoogle DeepMind, Research ScientistVerified email at google.com
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
Igor MordatchGoogle DeepMindVerified email at google.com
Harley WiltzerMcGill University, MilaVerified email at mila.quebec
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Max SchwarzerOpenAIVerified email at openai.com
Andre BarretoResearch Scientist, Google DeepMindVerified email at google.com
Will DabneyDeepMindVerified email at google.com
Yunhao TangResearch Scientist, DeepMindVerified email at columbia.edu
Johan S. Obando-CeronMila, University of MontrealVerified email at mila.quebec

Jesse Farebrother

Mila / McGill University; Meta AI

Verified email at cs.mcgill.ca - Homepage

Artificial Intelligence Machine Learning Reinforcement Learning


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Generalization and Regularization in DQN J Farebrother, MC Machado, M Bowling NeurIPS Workshop on Deep Reinforcement Learning, 2018	208	2018
Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning AA Taïga, R Agarwal, J Farebrother, A Courville, MG Bellemare International Conference on Learning Representations (ICLR), 2023	27	2023
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL J Farebrother*, J Orbay, Q Vuong, AA Taïga, Y Chebotar, T Xiao, A Irpan, ... International Conference on Machine Learning (ICML), 𝗢𝗿𝗮𝗹, 2024	26	2024
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks J Farebrother, J Greaves, R Agarwal, C Le Lan, R Goroshin, PS Castro, ... International Conference on Learning Representations (ICLR), 2023	25*	2023
Mixtures of Experts Unlock Parameter Scaling for Deep RL JS Obando-Ceron, G Sokar, T Willi*, C Lyle, J Farebrother, JN Foerster, ... International Conference on Machine Learning (ICML), 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁, 2024	10*	2024
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces CL Lan, J Greaves, J Farebrother, M Rowland, F Pedregosa, R Agarwal, ... International Conference on Artificial Intelligence and Statistics (AISTATS), 2023	5	2023
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy M Schwarzer, J Farebrother, J Greaves, ED Cubuk, R Agarwal, ... arXiv preprint arXiv:2311.17894, 2023	2*	2023
A Distributional Analogue to the Successor Representation H Wiltzer, J Farebrother, A Gretton, Y Tang, A Barreto, W Dabney, ... International Conference on Machine Learning (ICML), 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁, 2024	1	2024
Foundations of Multivariate Distributional Reinforcement Learning H Wiltzer, J Farebrother, A Gretton, M Rowland Neural Information Processing Systems (NeurIPS), 2024		2024
Revisiting Successor Features for Inverse Reinforcement Learning AK Jain, H Wiltzer, J Farebrother, I Rish, G Berseth, S Choudhury ICML 2024 Workshop on Models of Human Feedback for AI Alignment, 2024		2024

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