Wang, Binghui

22 publications

TMLR 2026 Theoretically Understanding Data Reconstruction Leakage in Federated Learning Binghui Zhang, Zifan Wang, Meng Pang, Yuan Hong, Binghui Wang

AAAI 2025 Breaking Data Silos in Parkinson's Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis Meng Pang, Houwei Xu, Zheng Huang, Yintao Zhou, Wei Huang, Binghui Wang

CVPR 2025 Deterministic Certification of Graph Neural Networks Against Graph Poisoning Attacks with Arbitrary Perturbations Jiate Li, Meng Pang, Yun Dong, Binghui Wang

AAAI 2025 Learning Robust and Privacy-Preserving Representations via Information Theory Binghui Zhang, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong, Binghui Wang

NeurIPS 2025 Measure-Theoretic Anti-Causal Representation Learning Arman Behnam, Binghui Wang

AAAI 2025 Practicable Black-Box Evasion Attacks on Link Prediction in Dynamic Graphs - A Graph Sequential Embedding Method Jiate Li, Meng Pang, Binghui Wang

ICLR 2025 Provably Robust Explainable Graph Neural Networks Against Graph Perturbation Attacks Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang

NeurIPS 2024 FedGMark: Certifiably Robust Watermarking for Federated Graph Learning Yuxin Yang, Qiang Li, Yuan Hong, Binghui Wang

ICLR 2024 GNNCert: Deterministic Certification of Graph Neural Networks Against Adversarial Perturbations Zaishuo Xia, Han Yang, Binghui Wang, Jinyuan Jia

ECCV 2024 Graph Neural Network Causal Explanation via Neural Causal Models Arman Behnam, Binghui Wang

ICML 2024 Graph Neural Network Explanations Are Fragile Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang

AAAI 2024 Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks Caridad Arroyo Arevalo, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong, Binghui Wang

CVPR 2023 IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients Ruo Yang, Binghui Wang, Mustafa Bilgic

WACV 2023 Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation Likun Zhang, Yahong Chen, Ang Li, Binghui Wang, Yiran Chen, Fenghua Li, Jin Cao, Ben Niu

CVPR 2023 Turning Strengths into Weaknesses: A Certified Robustness Inspired Attack Framework Against Graph Neural Networks Binghui Wang, Meng Pang, Yun Dong

ICLR 2022 Almost Tight L0-Norm Certified Robustness of Top-K Predictions Against Adversarial Perturbations Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Hongbin Liu, Neil Zhenqiang Gong

CVPR 2022 Bandits for Structure Perturbation-Based Black-Box Attacks to Graph Neural Networks with Theoretical Guarantees Binghui Wang, Youqi Li, Pan Zhou

ECCV 2022 UniCR: Universally Approximated Certified Robustness via Randomized Smoothing Hanbin Hong, Binghui Wang, Yuan Hong

AAAI 2021 Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong

CVPR 2021 Soteria: Provable Defense Against Privacy Leakage in Federated Learning from Representation Perspective Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, Yiran Chen

ICLR 2020 Certified Robustness for Top-K Predictions Against Adversarial Perturbations via Randomized Smoothing Jinyuan Jia, Xiaoyu Cao, Binghui Wang, Neil Zhenqiang Gong

NeurIPS 2020 Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability Nathan Inkawhich, Kevin Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, Yiran Chen