Daphne Cornelisse

11 papers A* 2A 1Journal 8
YearRankTypeTitle / Venue / Authors
2026 J jnl
Learning to Drive in New Cities Without Human Demonstrations.
CoRR
Zilin Wang, Saeed Rahmani, Daphne Cornelisse, Bidipta Sarkar, Alexander David Goldie, Jakob Nicolaus Foerster, Shimon Whiteson
2025 J jnl
Building reliable sim driving agents by scaling self-play.
CoRR
Daphne Cornelisse, Aarav Pandya, Kevin Joseph, Joseph Suárez, Eugene Vinitsky
2025 J jnl
Estimating cognitive biases with attention-aware inverse planning.
CoRR
Sounak Banerjee, Daphne Cornelisse, Deepak Edakkattil Gopinath, Emily Sumner, Jonathan A. DeCastro, Guy Rosman, Eugene Vinitsky, Mark K. Ho
2025 A* conf
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS.
ICLR
Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky
2024 J jnl
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS.
CoRR
Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky
2024 J jnl
Human-compatible driving agents through data-regularized self-play reinforcement learning.
RLJ
Daphne Cornelisse, Eugene Vinitsky
2024 J jnl
Human-compatible driving partners through data-regularized self-play reinforcement learning.
CoRR
Daphne Cornelisse, Eugene Vinitsky
2024 A conf
Pruning Neural Networks Using Cooperative Game Theory.
AAMAS
Mauricio Diaz-Ortiz Jr., Benjamin Kempinski, Daphne Cornelisse, Yoram Bachrach, Tal Kachman
2023 J jnl
Using Cooperative Game Theory to Prune Neural Networks.
CoRR
Mauricio Diaz-Ortiz Jr., Benjamin Kempinski, Daphne Cornelisse, Yoram Bachrach, Tal Kachman
2022 A* conf
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members.
NeurIPS
Daphne Cornelisse, Thomas Rood, Yoram Bachrach, Mateusz Malinowski, Tal Kachman
2022 J jnl
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members.
CoRR
Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, Tal Kachman