Learning to fail: Predicting fracture evolution in brittle materials using recurrent graph convolutional neural networks. - 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 (CoRR, 2018)

GEAR: Geometry-Aware Rényi Information. - Jose Gallego-Posada; Ankit Vani; Max Schwarzer; Simon Lacoste-Julien (CoRR, 2019)

GAIT: A Geometric Approach to Information Theory. - Jose Gallego-Posada; Ankit Vani; Max Schwarzer; Simon Lacoste-Julien (AISTATS, 2020)

Data-Efficient Reinforcement Learning with Momentum Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (CoRR, 2020)

Data-Efficient Reinforcement Learning with Self-Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (ICLR, 2021)

Iterated learning for emergent systematicity in VQA. - Ankit Vani; Max Schwarzer; Yuchen Lu; Eeshan Dhekane; Aaron C. Courville (ICLR, 2021)

Deep Reinforcement Learning at the Edge of the Statistical Precipice. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2021)

Pretraining Representations for Data-Efficient Reinforcement Learning. - Max Schwarzer; Nitarshan Rajkumar; Michael Noukhovitch; Ankesh Anand; Laurent Charlin; R. Devon Hjelm; Philip Bachman; Aaron C. Courville (NeurIPS, 2021)

Blockchain Technology in Supply Chain Management - A Discussion of Current and Future Research Topics. - Tan Gürpinar; Nick Große; Max Schwarzer; Eugen Burov; Roman Stammes; Philipp Asterios Ioannidis; Larissa Krämer; Rico Ahlbäumer; Michael Henke 004 (SmartCity360°, 2021)

The Primacy Bias in Deep Reinforcement Learning. - Evgenii Nikishin; Max Schwarzer; Pierluca D'Oro; Pierre-Luc Bacon; Aaron C. Courville (ICML, 2022)

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2022)

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. - Samuel Lavoie; Christos Tsirigotis; Max Schwarzer; Kenji Kawaguchi; Ankit Vani; Aaron C. Courville (CoRR, 2022)

Beyond Tabula Rasa: Reincarnating Reinforcement Learning. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (CoRR, 2022)

To join or not to join?-A framework for the evaluation of enterprise blockchain consortia. - Max Schwarzer; Tan Gürpinar; Michael Henke 004 (Frontiers Blockchain, 2022)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. - Pierluca D'Oro; Max Schwarzer; Evgenii Nikishin; Pierre-Luc Bacon; Marc G. Bellemare; Aaron C. Courville (ICLR, 2023)

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. - Samuel Lavoie; Christos Tsirigotis; Max Schwarzer; Ankit Vani; Michael Noukhovitch; Kenji Kawaguchi; Aaron C. Courville (ICLR, 2023)

Bigger, Better, Faster: Human-level Atari with human-level efficiency. - Max Schwarzer; Johan S. Obando-Ceron; Aaron C. Courville; Marc G. Bellemare; Rishabh Agarwal; Pablo Samuel Castro (ICML, 2023)

Large Language Models as Generalizable Policies for Embodied Tasks. - Andrew Szot; Max Schwarzer; Harsh Agrawal; Bogdan Mazoure; Walter Talbott; Katherine Metcalf; Natalie Mackraz; R. Devon Hjelm; Alexander Toshev (CoRR, 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)

MM1: Methods, Analysis and Insights from Multimodal LLM Pre-training. - Brandon McKinzie; Zhe Gan; Jean-Philippe Fauconnier; Sam Dodge; Bowen Zhang 002; Philipp Dufter; Dhruti Shah; Xianzhi Du; Futang Peng; Anton Belyi; Haotian Zhang 005; Karanjeet Singh 003; et al. (ECCV (29), 2024)

Large Language Models as Generalizable Policies for Embodied Tasks. - Andrew Szot; Max Schwarzer; Harsh Agrawal; Bogdan Mazoure; Rin Metcalf; Walter Talbott; Natalie Mackraz; R. Devon Hjelm; Alexander T. Toshev (ICLR, 2024)

MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training. - Brandon McKinzie; Zhe Gan; Jean-Philippe Fauconnier; Sam Dodge; Bowen Zhang 002; Philipp Dufter; Dhruti Shah; Xianzhi Du; Futang Peng; Floris Weers; Anton Belyi; Haotian Zhang 005; et al. (CoRR, 2024)

Competitive Programming with Large Reasoning Models. - Ahmed El-Kishky; Alexander Wei; Andre Saraiva 001; Borys Minaiev; Daniel Selsam; David Dohan; Francis Song; Hunter Lightman; Ignasi Clavera Gilaberte; Jakub Pachocki; Jerry Tworek; Lorenz Kuhn; et al. (CoRR, 2025)

gpt-oss-120b & gpt-oss-20b Model Card. - Sandhini Agarwal; Lama Ahmad; Jason Ai; Sam Altman; Andy Applebaum; Edwin Arbus; Rahul K. Arora; Yu Bai; Bowen Baker; Haiming Bao; Boaz Barak; Ally Bennett; et al. (CoRR, 2025)