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Retrieval-Augmented Reinforcement Learning


Mar 09, 2022
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell


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On the role of planning in model-based deep reinforcement learning


Nov 08, 2020
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, Théophane Weber


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Modular Meta-Learning with Shrinkage


Sep 12, 2019
Yutian Chen, Abram L. Friesen, Feryal Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas

* 14 pages (4 main, 8 supplement), under review 

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Deep Learning as a Mixed Convex-Combinatorial Optimization Problem


Apr 16, 2018
Abram L. Friesen, Pedro Domingos

* In Proceedings of the International Conference on Learning Representations (ICLR) 2018 
* 14 pages (9 body, 5 pages of references and appendices) 

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The Sum-Product Theorem: A Foundation for Learning Tractable Models


Nov 11, 2016
Abram L. Friesen, Pedro Domingos

* Proceedings of the 33rd International Conference on Machine Learning, pp. 1909-1918, 2016 
* 15 pages (10 body, 5 pages of appendices) 

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Recursive Decomposition for Nonconvex Optimization


Nov 08, 2016
Abram L. Friesen, Pedro Domingos

* Proceedings of the 24th International Joint Conference on Artificial Intelligence (2015), pp. 253-259 
* 11 pages, 7 figures, pdflatex 

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