Certifiable Accelerated Evaluation
Rigorous evaluation of intelligent physical systems against long-tail failures
C Xu, W Ding, W Lyu, Z Liu, S Wang, Y He, H Hu, D Zhao, B Li, ''SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles,'' Advances in Neural Information Processing Systems (NeurIPS), 2022. | |
M. Arief, Z. Huang, G. Kumar, Y. Bai, S. He, W. Ding, H. Lam, D. Zhao, ''Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems,'' Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, USA, 2021. | Bib |
Jiacheng Zhu, Jielin Qiu, Aritra Guha, Zhuolin Yang, XuanLong Nguyen, Bo Li, Ding Zhao , ''Interpolation for Robust Learning: Data Augmentation on Geodesics,'' Thirty-ninth International Conference on Machine Learning (ICML), 2023. | |
Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu and Ding Zhao, ''Robustness Certification of Visual Perception Models via Camera Motion Smoothing,'' Conference on Robot Learning (CoRL), 2022. | |
W. Ding, H. Lin, B. Li, D. Zhao, ''CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation,'' Conference on Robot Learning (CoRL), 2022. | |
Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao, ''Over-Conservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation,'' Management Science, 2023. | |
W Ding, C Xu, H Lin, B Li, D Zhao, ''A Survey on Safety-critical Driving Scenario Generation from Methodological Perspective,'' IEEE Transactions of Intelligent Transportation Systems, 2023. | |
Z Liu, Z Cen, V Isenbaev, W Liu, ZS Wu, B Li, D Zhao, ''Constrained variational policy optimization for safe reinforcement learning,'' Thirty-ninth International Conference on Machine Learning (ICML), 2022. | |
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li and Ding Zhao, ''On the Robustness of Safe Reinforcement Learning under Observational Perturbations,'' The International Conference on Learning Representations (ICLR), 2023. |
. 2022 ICML Safe Learning for Autonomous Driving Workshop (Best Paper Runner-up). 2022 NeurIPS ML Safety Workshop (AI Risk Analysis Award). |
Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao , '' Constrained Decision Transformer for Offline Safe RL,'' International Conference on Machine Learning (ICML), 2023. | |
Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao , ''Constrained Decision Transformer for Offline Safe Reinforcement Learning,'' International Conference on Machine Learning (ICML), 2023. | |
B. Chen, Z. Liu, J. Zhu, M. Xu, W. Ding, D. Zhao, ''Context-Aware Safe Reinforcement Learning for Non-Stationary Environments,'' 2021 International Conference on Robotics and Automation (ICRA), 2021. | Bib |
Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li and Ding Zhao, ''Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables,'' Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023, San Diego, USA, 2021. | |
M. Xu, W. Ding, J. Zhu, Z. Liu, B. Chen, D. Zhao, ''Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes,'' Advances in Neural Information Processing Systems (NeurIPS), 2020. | Bib |
Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao and Chuang Gan, ''Hyper-Decision Transformer for Efficient Online Policy Adaptation,'' The International Conference on Learning Representations (ICLR), 2023. | |
M. Xu, Y. Shen, S. Zhang, Y. Lu, D. Zhao, J, Tenenbaum, C. Gan, ''Prompting Decision Transformer for Few-shot Policy Generalization,'' Thirty-ninth International Conference on Machine Learning (ICML), 2022. | |
W. Ding, H. Lin, B. Li, D. Zhao, ''Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning,'' Advances in Neural Information Processing Systems (NeurIPS), 2022. | |
Rigorous evaluation of intelligent physical systems against long-tail failures
Use unsupervised learning, stochastic processing, and generative models to comprehend and generate naturalistic safety-critical environments
Design trustworthy intelligent autonomy for real world applications.
An open platform that facilitates development of Connected and Self-Driving Vehicles
Applications: multi-robot coordination in warehouse environment; food delivery robot on sidewalks.
Made by the people and for the people - drive harmoniously with human being