| A data-driven CHC solver H Zhu, S Magill, S Jagannathan ACM SIGPLAN Notices 53 (4), 707-721, 2018 | 97 | 2018 |
| An inductive synthesis framework for verifiable reinforcement learning H Zhu, Z Xiong, S Magill, S Jagannathan Proceedings of the 40th ACM SIGPLAN conference on programming language …, 2019 | 114 | 2019 |
| ART: abstraction refinement-guided training for provably correct neural networks X Lin, H Zhu, R Samanta, S Jagannathan # PLACEHOLDER_PARENT_METADATA_VALUE# 1, 148-157, 2020 | 31 | 2020 |
| AutoFlow: Automated Workflow Generation for Large Language Model Agents Z Li, S Xu, K Mei, W Hua, B Rama, O Raheja, H Wang, H Zhu, Y Zhang arXiv preprint arXiv:2407.12821, 2024 | | 2024 |
| Automatically learning shape specifications H Zhu, G Petri, S Jagannathan Proceedings of the 37th ACM SIGPLAN Conference on Programming Language …, 2016 | 38 | 2016 |
| Compositional abstraction refinement for timed systems F He, H Zhu, WNN Hung, X Song, M Gu 2010 4th IEEE International Symposium on Theoretical Aspects of Software …, 2010 | 15 | 2010 |
| Compositional and lightweight dependent type inference for ML H Zhu, S Jagannathan International Workshop on Verification, Model Checking, and Abstract …, 2013 | 38 | 2013 |
| Data mining based decomposition for assume-guarantee reasoning H Zhu, F He, WNN Hung, X Song, M Gu 2009 Formal Methods in Computer-Aided Design, 116-119, 2009 | 3 | 2009 |
| Defending observation attacks in deep reinforcement learning via detection and denoising Z Xiong, J Eappen, H Zhu, S Jagannathan Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 7 | 2022 |
| Dependent array type inference from tests H Zhu, AV Nori, S Jagannathan Verification, Model Checking, and Abstract Interpretation: 16th …, 2015 | 6 | 2015 |
| Differentiable synthesis of program architectures G Cui, H Zhu Advances in Neural Information Processing Systems 34, 11123-11135, 2021 | 18 | 2021 |
| Explain the explainer: Interpreting model-agnostic counterfactual explanations of a deep reinforcement learning agent Z Chen, F Silvestri, G Tolomei, J Wang, H Zhu, H Ahn IEEE Transactions on Artificial Intelligence 5 (4), 1443-1457, 2022 | 18 | 2022 |
| Graph collaborative reasoning H Chen, Y Li, S Shi, S Liu, H Zhu, Y Zhang Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 40 | 2022 |
| Instructing goal-conditioned reinforcement learning agents with temporal logic objectives W Qiu, W Mao, H Zhu Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Learn basic skills and reuse: Modularized adaptive neural architecture search (manas) H Chen, Y Li, H Zhu, Y Zhang Proceedings of the 31st ACM International Conference on Information …, 2022 | 7 | 2022 |
| Learning Program Specifications from Sample Runs H Zhu Purdue University, 2016 | | 2016 |
| Learning refinement types H Zhu, AV Nori, S Jagannathan ACM SIGPLAN Notices 50 (9), 400-411, 2015 | 36 | 2015 |
| Learning to verify the heap M Brockschmidt, Y Chen, B Cook, P Kohli, S Krishna, D Tarlow, H Zhu Technical Report, 2016 | 4 | 2016 |
| Poling: SMT Aided Linearizability Proofs H Zhu, G Petri, S Jagannathan Computer Aided Verification: 27th International Conference, CAV 2015, San …, 2015 | 30 | 2015 |
| Programmatic Reinforcement Learning with Formal Verification Y Wang, H Zhu | | |
