An always-updated list of my publications can be found here.
– Bigger, Better, Faster: Human-level Atari with human-level efficiency. Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal & Pablo Samuel Castro. ICML 2023.
– Sample Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. Pierluca D’Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc Bellemare & Aaron Courville. Oral presentation at ICLR 2023.
– Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi & Aaron Courville. Spotlight at ICLR 2023
– Beyond Tabula Rasa: Reincarnating Reinforcement Learning. Rishabh Agarwal, Max Schwarzer, Pablo Castro, Aaron Courville & Marc Bellemare. NeurIPS 2022.
– The Primacy Bias in Deep Reinforcement Learning. Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro*, Pierre-Luc Bacon & Aaron Courville. ICML 2022.
– Pretraining Representations for Data-Efficient Reinforcement Learning. Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, Devon Hjelm, Phillip Bachman & Aaron Courville. NeurIPS 2021.
– Deep Reinforcement Learning at the Statistical Precipice. Rishabh Agarwal, Max Schwarzer, Pablo Castro, Aaron Courville & Marc Bellemare. Outstanding Paper Award at NeurIPS 2021
– Data-Efficient Reinforcement Learning with Self-Predictive Representations. Max Schwarzer, Ankesh Anand, Rishab Goel, Devon Hjelm, Aaron Courville & Phillip Bachman. Spotlight presentation at ICLR 2021
– Iterated learning for emergent systematicity in VQA. Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane & Aaron Courville. Oral presentation at ICLR 2021
– GAIT: A Geometric Approach to Information Theory. Jose Gallego, Ankit Vani, Max Schwarzer & Simon Lacoste-Julien. Oral presentation at AISTATS 2020, Oral presentation at NeurIPS 2019 Workshop on Information Theory and Machine Learning
– Improving Text Simplification with Sentence Fusion. Max Schwarzer, Teerapaun Tanprasert & David Kauchak. NAACL Workshop on Graph-Based Methods for Natural Language Processing
– Learning to Fail: Predicting Fracture Evolution in Brittle Material Models using Recurrent Graph Convolutional Neural Networks. Max Schwarzer, Diana Lee, Bryce Rogan, Yadong Ruan, Zhengming Song & Allon Percus. Published in Computational Materials Science
– The Simplicity-Adequacy Tradeoff in Text Simplification. Max Schwarzer & David Kauchak.
Best undergraduate paper at 2018 Southern California NLP Symposium
– Robust Dendritic Computations with Sparse Distributed Representations. Subutai Ahmad, Max Schwarzer & Jeff Hawkins. Presented at Cosyne 2018 and OCNS 2018