Explaining Neural Networks Semantically and Quantitatively. - Runjin Chen; Hao Chen 099; Ge Huang; Jie Ren 018; Quanshi Zhang (ICCV, 2019)

Interpretable Complex-Valued Neural Networks for Privacy Protection. - Liyao Xiang; Hao Zhang 063; Haotian Ma; Yifan Zhang; Jie Ren 018; Quanshi Zhang (ICLR, 2020)

A Unified Approach to Interpreting and Boosting Adversarial Transferability. - Xin Wang 108; Jie Ren 018; Shuyun Lin; Xiangming Zhu 002; Yisen Wang 001; Quanshi Zhang (ICLR, 2021)

Interpreting and Disentangling Feature Components of Various Complexity from DNNs. - Jie Ren 018; Mingjie Li; Zexu Liu; Quanshi Zhang (ICML, 2021)

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Yiting Chen 003; Xu Cheng 005; Xin Wang 108; Meng Zhou; Jie Shi; Quanshi Zhang (NeurIPS, 2021)

Game-theoretic Understanding of Adversarially Learned Features. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Xu Cheng 005; Xin Wang 108; Yiting Chen 003; Jie Shi; Quanshi Zhang (CoRR, 2021)

Learning Baseline Values for Shapley Values. - Jie Ren 018; Zhanpeng Zhou; Qirui Chen; Quanshi Zhang (CoRR, 2021)

A Game-Theoretic Taxonomy of Visual Concepts in DNNs. - Xu Cheng 005; Chuntung Chu; Yi Zheng; Jie Ren 018; Quanshi Zhang (CoRR, 2021)

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. - Quanshi Zhang; Tian Han 001; Lixin Fan; Zhanxing Zhu; Hang Su; Ying Nian Wu; Jie Ren 018; Hao Zhang 063 (CoRR, 2021)

Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models. - Jie Ren 018; Mingjie Li; Qirui Chen; Huiqi Deng; Quanshi Zhang (CoRR, 2021)

Discovering and Explaining the Representation Bottleneck of DNNs. - Huiqi Deng; Qihan Ren; Xu Chen; Hao Zhang 063; Jie Ren 018; Quanshi Zhang (CoRR, 2021)

Trap of Feature Diversity in the Learning of MLPs. - Dongrui Liu; Shaobo Wang 001; Jie Ren 018; Kangrui Wang; Sheng Yin; Quanshi Zhang (CoRR, 2021)

Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering. - Quanshi Zhang; Jie Ren 018; Ge Huang; Ruiming Cao; Ying Nian Wu; Song-Chun Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. - Jie Ren 018; Mingjie Li; Meng Zhou; Shih-Han Chan; Quanshi Zhang (ICML, 2022)

Why Adversarial Training of ReLU Networks Is Difficult? - Xu Cheng 005; Hao Zhang 063; Yue Xin; Wen Shen 002; Jie Ren 018; Quanshi Zhang (CoRR, 2022)

Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability. - Quanshi Zhang; Xin Wang 108; Jie Ren 018; Xu Cheng 005; Shuyun Lin; Yisen Wang 001; Xiangming Zhu 002 (CoRR, 2022)

Defining and Quantifying the Emergence of Sparse Concepts in DNNs. - Jie Ren 018; Mingjie Li; Qirui Chen; Huiqi Deng; Quanshi Zhang (CVPR, 2023)

Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? - Jie Ren 018; Zhanpeng Zhou; Qirui Chen; Quanshi Zhang (ICLR, 2023)

Identifying Semantic Induction Heads to Understand In-Context Learning. - Jie Ren 018; Qipeng Guo; Hang Yan 001; Dongrui Liu; Quanshi Zhang; Xipeng Qiu; Dahua Lin (ACL (Findings), 2024)

Identifying Semantic Induction Heads to Understand In-Context Learning. - Jie Ren 018; Qipeng Guo; Hang Yan 001; Dongrui Liu; Xipeng Qiu; Dahua Lin (CoRR, 2024)

Interpretability of Neural Networks Based on Game-theoretic Interactions. - Huilin Zhou; Jie Ren 018; Huiqi Deng; Xu Cheng 005; Jinpeng Zhang; Quanshi Zhang (Mach. Intell. Res., 2024)

Monitoring Primitive Interactions During the Training of DNNs. - Jie Ren 018; Xinhao Zheng; Jiyu Liu; Andrew Lizarraga; Ying Nian Wu; Liang Lin; Quanshi Zhang (AAAI, 2025)