| An intuitive guide to convolutional neural networks D Cornelisse free Code Camp, 1-19, 2018 | 73 | 2018 |
| Neural payoff machines: Predicting fair and stable payoff allocations among team members D Cornelisse, T Rood, Y Bachrach, M Malinowski, T Kachman Advances in Neural Information Processing Systems 35, 25491-25503, 2022 | 10 | 2022 |
| Gpudrive: Data-driven, multi-agent driving simulation at 1 million fps S Kazemkhani, A Pandya, D Cornelisse, B Shacklett, E Vinitsky International Conference on Learning Representations (ICLR) 2025, 2024 | 6 | 2024 |
| Human-compatible driving partners through data-regularized self-play reinforcement learning D Cornelisse, E Vinitsky Reinforcement Learning Journal (RLJ) 5, 2320--2344, 2024 | 5 | 2024 |
| Using cooperative game theory to prune neural networks M Diaz-Ortiz Jr, B Kempinski, D Cornelisse, Y Bachrach, T Kachman arXiv preprint arXiv:2311.10468, 2023 | 2 | 2023 |
| Pruning Neural Networks Using Cooperative Game Theory M Diaz-Ortiz Jr, B Kempinski, D Cornelisse, Y Bachrach, T Kachman Proceedings of the 23rd International Conference on Autonomous Agents and …, 2024 | 1 | 2024 |
| Building reliable sim driving agents by scaling self-play D Cornelisse, A Pandya, K Joseph, J Suárez, E Vinitsky arXiv preprint arXiv:2502.14706, 2025 | | 2025 |
| Strategic Foundation Models D Goktas, A Greenwald, T Osogami, R Patel, K Leyton-Brown, ... | | 2025 |
Eugene VinitskyAssistant Professor, NYUVerified email at nyu.edu
Eugene VinitskyAssistant Professor, NYUVerified email at nyu.edu