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Max Schwarzer
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Beyond Tabula Rasa: Reincarnating Reinforcement Learning


Jun 03, 2022
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare

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The Primacy Bias in Deep Reinforcement Learning


May 16, 2022
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville

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* ICML 2022; code at https://github.com/evgenii-nikishin/rl_with_resets 
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Simplicial Embeddings in Self-Supervised Learning and Downstream Classification


Apr 01, 2022
Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Kenji Kawaguchi, Ankit Vani, Aaron Courville

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* 22 pages, 5 figures, 5 tables, Preprint 
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Deep Reinforcement Learning at the Edge of the Statistical Precipice


Aug 30, 2021
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare

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Pretraining Representations for Data-Efficient Reinforcement Learning


Jun 09, 2021
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, Devon Hjelm, Philip Bachman, Aaron Courville

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Iterated learning for emergent systematicity in VQA


May 03, 2021
Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

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* Published as a conference paper at ICLR 2021. 9 pages main, 21 pages total including references and appendix 
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Data-Efficient Reinforcement Learning with Momentum Predictive Representations


Jul 12, 2020
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, Philip Bachman

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* The first two authors contributed equally to this work 
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GEAR: Geometry-Aware Rényi Information


Jun 19, 2019
Jose Gallego, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien

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Learning to fail: Predicting fracture evolution in brittle materials using recurrent graph convolutional neural networks


Oct 14, 2018
Max Schwarzer, Bryce Rogan, Yadong Ruan, Zhengming Song, Diana Lee, Allon G. Percus, Viet T. Chau, Bryan A. Moore, Esteban Rougier, Hari S. Viswanathan, Gowri Srinivasan

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