Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. - Daphne Cornelisse; Thomas Rood; Yoram Bachrach; Mateusz Malinowski; Tal Kachman (NeurIPS, 2022)

Using Cooperative Game Theory to Prune Neural Networks. - Mauricio Diaz-Ortiz Jr.; Benjamin Kempinski; Daphne Cornelisse; Yoram Bachrach; Tal Kachman (CoRR, 2023)

Pruning Neural Networks Using Cooperative Game Theory. - Mauricio Diaz-Ortiz Jr.; Benjamin Kempinski; Daphne Cornelisse; Yoram Bachrach; Tal Kachman (AAMAS, 2024)

Human-compatible driving agents through data-regularized self-play reinforcement learning. - Daphne Cornelisse; Eugene Vinitsky (RLJ, 2024)

Human-compatible driving partners through data-regularized self-play reinforcement learning. - Daphne Cornelisse; Eugene Vinitsky (CoRR, 2024)

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS. - Saman Kazemkhani; Aarav Pandya; Daphne Cornelisse; Brennan Shacklett; Eugene Vinitsky (ICLR, 2025)

Building reliable sim driving agents by scaling self-play. - Daphne Cornelisse; Aarav Pandya; Kevin Joseph; Joseph Suárez; Eugene Vinitsky (CoRR, 2025)