Daphne Cornelisse — accepted AI-conference papers

2025

Estimating cognitive biases with attention-aware inverse planning

Sounak Banerjee, Daphne Cornelisse, Deepak Edakkattil Gopinath, Emily Sumner, Jonathan DeCastro, Guy Rosman +2

NeurIPS 2025spotlight

People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks suc…

Cited by 0 Source ↗Scholar ↗

2025

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS

Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky

ICLR 2025poster

Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experien…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2022

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

Daphne Cornelisse, Thomas Rood, Yoram Bachrach, Mateusz Malinowski, Tal Kachman

NeurIPS 2022accept

In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of agents and allocating them shares of the reward that promote long-lasting cooperation are difficult tasks. Cooperative…

Cited by 10 Source ↗PDF ↗Scholar ↗