Binghui Wang
20 papers · 2020–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge π Academic Marathon (5) π Renaissance Researcher (8) π Conference Polyglot (7) πΊοΈ Taxonomy Completionist (31)
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Taxonomy Completionist
(31)
π§
Keyword Pioneer
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Grand Slam
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Keyword Champion
π
Triple Crown
ποΈ
Keyword Collector
(65)
π₯
Unstoppable
(6)
β‘
Prolific Year
(5)
π
Century Club
(20)
Conferences
AAAI (5)
CVPR (5)
ICLR (4)
ECCV (2)
NIPS (2)
ICML (1)
WACV (1)
Top co-authors
Research topics
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 Graphsβa 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 Parkinsonβs 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