He Zhu

According to our database, He Zhu authored at least 21 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent.

Ziheng Chen, Fabrizio Silvestri, Gabriele Tolomei, Jia Wang, He Zhu, Hongshik Ahn

IEEE Trans. Artif. Intell., April, 2024

2023

Verification-guided Programmatic Controller Synthesis.

Yuning Wang, He Zhu

Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2023

Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives.

Wenjie Qiu, Wensen Mao, He Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Graph Collaborative Reasoning.

Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang

Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising.

Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Programmatic Reinforcement Learning without Oracles.

Wenjie Qiu, He Zhu

Proceedings of the Tenth International Conference on Learning Representations, 2022

ReLAX: Reinforcement Learning Agent Explainer for Arbitrary Predictive Models.

Ziheng Chen, Fabrizio Silvestri, Jia Wang, He Zhu, Hongshik Ahn, Gabriele Tolomei

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS).

Hanxiong Chen, Yunqi Li, He Zhu, Yongfeng Zhang

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

ReLACE: Reinforcement Learning Agent for Counterfactual Explanations of Arbitrary Predictive Models.

Ziheng Chen, Fabrizio Silvestri, Gabriele Tolomei, He Zhu, Jia Wang, Hongshik Ahn

CoRR, 2021

Differentiable Synthesis of Program Architectures.

Guofeng Cui, He Zhu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Robustness to Adversarial Attacks in Learning-Enabled Controllers.

Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan

CoRR, 2020

Art: Abstraction Refinement-Guided Training for Provably Correct Neural Networks.

Xuankang Lin, He Zhu, Roopsha Samanta, Suresh Jagannathan

Proceedings of the 2020 Formal Methods in Computer Aided Design, 2020

2019

An inductive synthesis framework for verifiable reinforcement learning.

He Zhu, Zikang Xiong, Stephen Magill, Suresh Jagannathan

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2019

2018

A data-driven CHC solver.

He Zhu, Stephen Magill, Suresh Jagannathan

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2018

2016

Automatically learning shape specifications.

He Zhu, Gustavo Petri, Suresh Jagannathan

Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2016

2015

Dependent Array Type Inference from Tests.

He Zhu, Aditya V. Nori, Suresh Jagannathan

Proceedings of the Verification, Model Checking, and Abstract Interpretation, 2015

Learning refinement types.

He Zhu, Aditya V. Nori, Suresh Jagannathan

Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming, 2015

Poling: SMT Aided Linearizability Proofs.

He Zhu, Gustavo Petri, Suresh Jagannathan

Proceedings of the Computer Aided Verification - 27th International Conference, 2015

2013

Compositional and Lightweight Dependent Type Inference for ML.

He Zhu, Suresh Jagannathan

Proceedings of the Verification, 2013

2010

Compositional Abstraction Refinement for Timed Systems.

Fei He, He Zhu, William N. N. Hung, Xiaoyu Song, Ming Gu

Proceedings of the 4th IEEE International Symposium on Theoretical Aspects of Software Engineering, 2010

2009

Data mining based decomposition for assume-guarantee reasoning.

He Zhu, Fei He, William N. N. Hung, Xiaoyu Song, Ming Gu

Proceedings of 9th International Conference on Formal Methods in Computer-Aided Design, 2009