| Proving expected sensitivity of probabilistic programs with randomized variable-dependent termination time P Wang, H Fu, K Chatterjee, Y Deng, M Xu Proceedings of the ACM on Programming Languages 4 (POPL), 2019 | 60 | 2019 |
| Cost analysis of nondeterministic probabilistic programs P Wang, H Fu, AK Goharshady, K Chatterjee, X Qin, W Shi Proceedings of the 40th ACM SIGPLAN Conference on Programming Language …, 2019 | 52 | 2019 |
| Tail-Bound Cost Analysis over Nondeterministic Probabilistic Programs P Wang Journal of Shanghai Jiaotong University (Science) 28 (6), 772-782, 2023 | 3 | 2023 |
| Unifying qualitative and quantitative safety verification of dnn-controlled systems D Zhi, P Wang, S Liu, L Ong, M Zhang arXiv preprint arXiv:2404.01769, 2024 | 1 | 2024 |
| Taming reachability analysis of dnn-controlled systems via abstraction-based training J Tian, D Zhi, S Liu, P Wang, G Katz, M Zhang International Conference on Verification, Model Checking, and Abstract …, 2023 | 1 | 2023 |
| Boosting verification of deep reinforcement learning via piece-wise linear decision neural networks J Tian, D Zhi, S Liu, P Wang, C Chen, M Zhang Advances in Neural Information Processing Systems 36, 10022-10037, 2023 | 1 | 2023 |
| BBReach: Tight and Scalable Black-Box Reachability Analysis of Deep Reinforcement Learning Systems J Tian, D Zhi, S Liu, P Wang, G Katz, M Zhang CoRR, abs/2211.11127, 2022 | 1 | 2022 |
| Robustness Verification of Deep Reinforcement Learning Based Control Systems Using Reward Martingales D Zhi, P Wang, C Chen, M Zhang Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 19992 …, 2024 | | 2024 |
| Template-Based Static Posterior Inference for Bayesian Probabilistic Programming P Wang, H Fu, T Yang, G Li, L Ong arXiv preprint arXiv:2307.13160, 2023 | | 2023 |
| Static Analysis of Posterior Inference in Bayesian Probabilistic Programming P Wang, H Fu, T Yang, G Li, L Ong | | 2023 |
Hongfei FuShanghai Jiao Tong UniversityE-mail confirmado em cs.sjtu.edu.cn
