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ORCIDDaphne Cornelisse
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2020 – today
- 2024
[j1]Daphne Cornelisse, Eugene Vinitsky:
Human-compatible driving agents through data-regularized self-play reinforcement learning. RLJ 5: 2320-2344 (2024)- [c2]Mauricio Diaz-Ortiz Jr., Benjamin Kempinski, Daphne Cornelisse, Yoram Bachrach, Tal Kachman:
Pruning Neural Networks Using Cooperative Game Theory. AAMAS 2024: 2243-2245 - [i4]Daphne Cornelisse, Eugene Vinitsky:
Human-compatible driving partners through data-regularized self-play reinforcement learning. CoRR abs/2403.19648 (2024) - [i3]Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky:
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS. CoRR abs/2408.01584 (2024) - 2023
- [i2]Mauricio Diaz-Ortiz Jr., Benjamin Kempinski, Daphne Cornelisse, Yoram Bachrach, Tal Kachman:
Using Cooperative Game Theory to Prune Neural Networks. CoRR abs/2311.10468 (2023) - 2022
- [c1]Daphne Cornelisse, Thomas Rood, Yoram Bachrach, Mateusz Malinowski, Tal Kachman:
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. NeurIPS 2022 - [i1]Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, Tal Kachman:
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. CoRR abs/2208.08798 (2022)
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