← Authors

Binghui Wang

20 papers · 2020–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (5) 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (31) 🧭 Keyword Pioneer πŸ† Grand Slam πŸ† Keyword Champion πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (65) πŸ”₯ Unstoppable (6) ⚑ Prolific Year (5) πŸ’Ž Century Club (20)

Conferences

AAAI (5) CVPR (5) ICLR (4) ECCV (2) NIPS (2) ICML (1) WACV (1)

Top co-authors

Meng Pang (6) Yun Dong (6) Jinyuan Jia (6) Yuan Hong (4) Jiate Li (4) Neil Zhenqiang Gong (3) Yiran Chen (3) Sayedeh Leila Noorbakhsh (2) Ang Li (2) Xiaoyu Cao (2)

Research topics

Privacy (2)

Keywords

graph neural network (4) privacy preservation (3) federated learning (3) adversarial attack (3) representation learning (3) evasion attack (2) poisoning attack (2) black-box attack (2) attribute inference (2) adversarial learning (1) distributed learning (1) link prediction (1) feature disentanglement (1) privacy-preserving learning (1) deep reinforcement learning (1) label noise handling (1) fraud detection (1) graph classification (1) saliency map (1) graph embedding (1)

Papers

Learning Robust and Privacy-Preserving Representations via Information Theory AAAI 2025
Practicable Black-Box Evasion Attacks on Link Prediction in Dynamic Graphsβ€”a Graph Sequential Embedding Method AAAI 2025
Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks ICLR 2025
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations CVPR 2025
Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis AAAI 2025
FedGMark: Certifiably Robust Watermarking for Federated Graph Learning NIPS 2024
Graph Neural Network Causal Explanation via Neural Causal Models ECCV 2024
Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks AAAI 2024
GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations ICLR 2024
Graph Neural Network Explanations are Fragile ICML 2024
Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation WACV 2023
Turning Strengths Into Weaknesses: A Certified Robustness Inspired Attack Framework Against Graph Neural Networks CVPR 2023
IDGI: A Framework To Eliminate Explanation Noise From Integrated Gradients CVPR 2023
Bandits for Structure Perturbation-Based Black-Box Attacks To Graph Neural Networks With Theoretical Guarantees CVPR 2022
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations ICLR 2022
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing ECCV 2022
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective CVPR 2021
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks AAAI 2021
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability NIPS 2020
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing ICLR 2020