Data mining based decomposition for assume-guarantee reasoning. - He Zhu 001; Fei He 001; William N. N. Hung; Xiaoyu Song; Ming Gu 001 (FMCAD, 2009)

Compositional Abstraction Refinement for Timed Systems. - Fei He 001; He Zhu 001; William N. N. Hung; Xiaoyu Song; Ming Gu 001 (TASE, 2010)

Compositional and Lightweight Dependent Type Inference for ML. - He Zhu 001; Suresh Jagannathan (VMCAI, 2013)

Poling: SMT Aided Linearizability Proofs. - He Zhu 001; Gustavo Petri; Suresh Jagannathan (CAV (2), 2015)

Learning refinement types. - He Zhu 001; Aditya V. Nori; Suresh Jagannathan (ICFP, 2015)

Dependent Array Type Inference from Tests. - He Zhu 001; Aditya V. Nori; Suresh Jagannathan (VMCAI, 2015)

Automatically learning shape specifications. - He Zhu 001; Gustavo Petri; Suresh Jagannathan (PLDI, 2016)

Learning Program Specifications from Sample Runs - He Zhu 001 (2016)

A data-driven CHC solver. - He Zhu 001; Stephen Magill; Suresh Jagannathan (PLDI, 2018)

An inductive synthesis framework for verifiable reinforcement learning. - He Zhu 001; Zikang Xiong; Stephen Magill; Suresh Jagannathan (PLDI, 2019)

Art: Abstraction Refinement-Guided Training for Provably Correct Neural Networks. - Xuankang Lin; He Zhu 001; Roopsha Samanta; Suresh Jagannathan (FMCAD, 2020)

Robustness to Adversarial Attacks in Learning-Enabled Controllers. - Zikang Xiong; Joe Eappen; He Zhu 001; Suresh Jagannathan (CoRR, 2020)

Differentiable Synthesis of Program Architectures. - Guofeng Cui; He Zhu 001 (NeurIPS, 2021)

ReLACE: Reinforcement Learning Agent for Counterfactual Explanations of Arbitrary Predictive Models. - Ziheng Chen; Fabrizio Silvestri; Gabriele Tolomei; He Zhu 001; Jia Wang 009; Hongshik Ahn (CoRR, 2021)

Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS). - Hanxiong Chen; Yunqi Li 003; He Zhu 001; Yongfeng Zhang (CIKM, 2022)

ReLAX: Reinforcement Learning Agent Explainer for Arbitrary Predictive Models. - Ziheng Chen; Fabrizio Silvestri; Jia Wang 009; He Zhu 001; Hongshik Ahn; Gabriele Tolomei (CIKM, 2022)

Programmatic Reinforcement Learning without Oracles. - Wenjie Qiu 002; He Zhu 001 (ICLR, 2022)

Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising. - Zikang Xiong; Joe Eappen; He Zhu 001; Suresh Jagannathan (ECML/PKDD (3), 2022)

Graph Collaborative Reasoning. - Hanxiong Chen; Yunqi Li 003; Shaoyun Shi; Shuchang Liu 001; He Zhu 001; Yongfeng Zhang (WSDM, 2022)

Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives. - Wenjie Qiu 002; Wensen Mao; He Zhu 001 (NeurIPS, 2023)

Verification-guided Programmatic Controller Synthesis. - Yuning Wang; He Zhu 001 (TACAS (2), 2023)

Safe Exploration in Reinforcement Learning by Reachability Analysis over Learned Models. - Yuning Wang; He Zhu 001 (CAV (3), 2024)

Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning. - Yuanlin Duan; Guofeng Cui; He Zhu 001 (NeurIPS, 2024)

Learning World Models for Unconstrained Goal Navigation. - Yuanlin Duan; Wensen Mao; He Zhu 001 (NeurIPS, 2024)

Reward-Guided Synthesis of Intelligent Agents with Control Structures. - Guofeng Cui; Yuning Wang; Wenjie Qiu 002; He Zhu 001 (Proc. ACM Program. Lang., 2024)

Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent. - Ziheng Chen; Fabrizio Silvestri; Gabriele Tolomei; Jia Wang 009; He Zhu 001; Hongshik Ahn (IEEE Trans. Artif. Intell., 2024)

Deductive Synthesis of Reinforcement Learning Agents for Infinite Horizon Tasks. - Yuning Wang; He Zhu 001 (CAV (4), 2025)