Jesse Farebrother

According to our database, Jesse Farebrother authored at least 12 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching.

Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury

CoRR, 2024

CALE: Continuous Arcade Learning Environment.

Jesse Farebrother, Pablo Samuel Castro

CoRR, 2024

Foundations of Multivariate Distributional Reinforcement Learning.

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland

CoRR, 2024

A Distributional Analogue to the Successor Representation.

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.

Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore

CoRR, 2023

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning.

Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2018

Generalization and Regularization in DQN.

Jesse Farebrother, Marlos C. Machado, Michael Bowling

CoRR, 2018

Using biconnected components for efficient identification of upstream features in large spatial networks (GIS cup).

Zach Goldthorpe, Jason Cannon, Jesse Farebrother, Zachary Friggstad, Mario A. Nascimento

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018