J. Chi, K. Wang*, et.al.，Activations as Features: Probing LLMs for Generalizable Essay Scoring Representations，the 40th AAAI Conference on Artificial Intelligence (AAAI 2026).
J.T.Wu, ..., K. Wang*, Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints, The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026).
J.T.Wu, X.T. Huang, Yu Chen, S. Pang, K. Wang*, Scaling and Taming Adversarial Training with Synthetic Data, International Conference on Computer Vision (ICCV 2025).
J.T. Wu, Z.Y.Song, X.Y. Zhang, S.J.Xie, L.X. Lin, K. Wang*, Vision Transformers Beat WideResNets on Small Scale Datasets Adversarial Robustness, the 39th AAAI Conference on Artificial Intelligence (AAAI 2025).
Y. Chen, K. Wang*, et.al.，Semantic-Guided Fast Adversarial Training via Class Relationship Exploitation, 16th ACM International Conference on Multimedia Retrieval (ICMR 2026).
Y. Chen, K. Wang*, et.al.，BridgeARD: Bridging the Latent Gap for Dual-Teacher Adversarial Robust Distillation, International Conference on Multimedia and Expo (ICME 2026).
Y. Chen, K. Wang*,et.al.，Feature Vulnerability-Aware Adversarial Training, International Conference on Multimedia and Expo (ICME 2026).
Y.Shuai, K. Wang*,et.al.，GAformer: Low-Light Image Enhancement Based on Gradient-Aware Kernel and Frequency-Modulated Transformer, 16th ACM International Conference on Multimedia Retrieval (ICMR 2026).
D.H. Zhou, B.W. Wu, K. Wang*,et.al.，Intervention-Driven Correlation Reduction: A Data Generation Approach for Achieving Counterfactually Fair Predictors, International Conference on Data Engineering(ICDE 2025).
K. Wang, et.al.,A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability, IEEE Transactions on Neural Networks and Learning Systems, 36(2): 2118-2132, 2024.
K. Wang, et.al.,Score-based Counterfactual Generation for Interpretable Medical Image Classification and Lesion Localization, IEEE Transactions on Medical Imaging, 43(10): 3596-3607, 2024.
K. Wang, et.al., Interpreting Adversarial Examples and Robustness for Deep Learning-based Auto-Driving Systems, IEEE Transactions on Intelligent Transportation Systems (T-ITS), 23(7):9755-9764,2022.
K. Wang, et.al., AFFIRM: Provably Forward Privacy for Searchable Encryption in Cooperative Intelligent Transportation System, IEEE Transactions on Intelligent Transportation Systems (T-ITS), 23(11):22607-22618, 2022.
K. Wang, et.al.，Statistics-Physics-Based Interpretation of the Classification Reliability of Convolutional Neural Networks in Industrial Automation Domain, IEEE Transactions on Industrial Informatics (TII),19(2):2165-2172, 2023.
K. Wang, et.al., Voice-Transfer Attacking on Industrial Voice Control Systems in 5G-Aided IIoT Domain, IEEE Transactions on Industrial Informatics (TII), 17(10):7085-7092, 2021.
K. Wang, et.al., Optimizing Neural Network Training: A Markov Chain Approach for Resource Conservation, IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2024.3413688, 2024.
K. Wang,et.al., Forward Privacy Preservation in IoT enabled Healthcare Systems, IEEE Transactions on Industrial Informatics (TII), 18(3):1991-1999, 2022.
P.Xu, K. Wang*, et.al., Adversarial Robustness in Graph-Based Neural Architecture Search for Edge AI Transportation Systems, IEEE Transactions on Intelligent Transportation Systems (T-ITS), 24(8): 8465-8474, 2023.
P. Xu, K. Wang*, et.al., An Interpretive Perspective: Adversarial Trojaning Attack on Neural-Architecture-Search Enabled Edge AI Systems, IEEE Transactions on Industrial Informatics (TII), 19(1):503-510,2023.
K. Wang, et.al., Uncovering Hidden Vulnerabilities in Convolutional Neural Networks through Graph-based Adversarial Robustness Evaluation, Pattern Recognition,143,109745, 2023.
K. Wang, et.al., Neural Architecture Search Based Multiobjective Cognitive Automation System, IEEE Systems Journal, 15(2):2918-2925, 2021.
K. Wang, et.al.，A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain, IEEE Network, 35(1):204-210, 2021.
K. Wang, et.al., A Trusted Consensus Fusion Scheme for Decentralized Collaborative Learning in Massive IoT Domain, Information Fusion, 72(1):100-109, 2021.
