| World model as a graph: Learning latent landmarks for planning L Zhang, G Yang, BC Stadie International conference on machine learning, 12611-12620, 2021 | 86 | 2021 |
| Generative verifiers: Reward modeling as next-token prediction L Zhang, A Hosseini, H Bansal, M Kazemi, A Kumar, R Agarwal International Conference on Learning Representations, 2024 | 47* | 2024 |
| Learning unsupervised world models for autonomous driving via discrete diffusion L Zhang, Y Xiong, Z Yang, S Casas, R Hu, R Urtasun International Conference on Learning Representations, 2023 | 42 | 2023 |
| Towards unsupervised object detection from lidar point clouds L Zhang, AJ Yang, Y Xiong, S Casas, B Yang, M Ren, R Urtasun Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 33 | 2023 |
| Learning intrinsic rewards as a bi-level optimization problem B Stadie, L Zhang, J Ba Conference on Uncertainty in Artificial Intelligence, 111-120, 2020 | 24 | 2020 |
| Learning realistic traffic agents in closed-loop C Zhang, J Tu, L Zhang, K Wong, S Suo, R Urtasun 7th Annual Conference on Robot Learning, 2023 | 15 | 2023 |
| Learning to drive via asymmetric self-play C Zhang, S Biswas, K Wong, K Fallah, L Zhang, D Chen, S Casas, ... European Conference on Computer Vision (ECCV), 2024 | | 2024 |
| World Model as a Graph: Learning Latent Landmarks for Planning Supplementary Materials L Zhang, G Yang, B Stadie | | |
Raquel UrtasunFounder & CEO Waabi, Professor, University of Toronto.Verified email at cs.toronto.edu