• TMLR 2026 When Active Learning Meets Graph Similarity: Evidential Variance for Graph Selection
  Chengtai Cao, Haoyu Yang, Shenglin Wang, Xinglin Lian, and Fan Zhou

• PhD Thesis Enhancing Decision Intelligence in Autonomous Driving through Uncertainty Modeling and Explainable Actions (透過不確定性建模與可解釋的行動來提升自動駕駛的決策智能)
  Chengtai Cao

• IJCAI 2026 No More Shortcuts: Network Traffic Anomaly Detection via Bidirectional Prediction
  Xinglin Lian, Chengtai Cao, Fanglin Yu, Ting Zhong, and Fan Zhou

• TDSC 2026 Shattering Weak Facades: Trustworthy Detection of Encrypted Malicious Traffic via Uncertainty-Aware Fusion
  Meihui Zhong, Chengtai Cao, Wenxin Tai, and Fan Zhou

• INFOCOM 2026 Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic
  Xinglin Lian, Chengtai Cao, Ting Zhong, Yong Wang, Kai Chen, and Fan Zhou

• AAAI 2026 Continuous Vision-Language-Action Co-Learning with Semantic-Physical Alignment for Behavioral Cloning
  Xiuxiu Qi, Yu Yang, Jiannong Cao, Luyao Bai, Chongshan Fan, Chengtai Cao, and Hongpeng Wang

• ECAI 2025 DAMO: Dual-Attention with Multi-Objective Optimization for Explainable Autonomous Driving
  Chengtai Cao, Shenglin Wang, Xinhong Chen, Yung-Hui Li, and Jianping Wang

• WWW 2025 Facing Anomalies Head-On: Network Traffic Anomaly Detection via Uncertainty-Inspired Inter-Sample Differences
  Xinglin Lian, Chengtai Cao, Yan Liu, Xovee Xu, Yu Zheng, and Fan Zhou

• CSUR 2024 A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
  Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang, and Kunpeng Zhang

• IJCAI 2024 SGDCL: Semantic-Guided Dynamic Correlation Learning for Explainable Autonomous Driving
  Chengtai Cao, Xinhong Chen, Jianping Wang, Qun Song, Rui Tan, and Yung-Hui Li

• AAAI 2024 CCTR: Calibrating Trajectory Prediction for Uncertainty-Aware Motion Planning in Autonomous Driving
  Chengtai Cao, Xinhong Chen, Jianping Wang, Qun Song, Rui Tan, and Yung-Hui Li

• AAAI 2021 Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
  Fan Zhou and Chengtai Cao* (Corresponding Author)

• ESWA 2021 Learning Meta-Knowledge for Few-shot Image Emotion Recognition
  Fan, Zhou, Chengtai Cao, Ting Zhong, and Ji Geng

• ICC 2021 Trajectory-User Linking via Graph Neural Network
  Fan Zhou, Shupei Chen, Jin Wu, Chengtai Cao, and Shengming Zhang

• Master Thesis Application of Graph Neural Network Base on Meta-learning (基于元学习的图神经网络应用研究)
  Chengtai Cao

• INFOCOM 2020 Fast Network Alignment via Graph Meta-learning
  Fan Zhou, Chengtai Cao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Ji Geng

• CIKM 2019 Meta-GNN: on Few-shot Node Classification in Graph Meta-Learning
  Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng

Patents

• China Invention Patent, No. CN112613556B: Few-Shot Image Sentiment Classification Method Based on Meta-Learning (基于元学习的少样本图像情感分类方法). Fan Zhou, Chengtai Cao, Ting Zhong, and Tianliang Wang
• China Invention Patent, No. CN113095440B: Training Data Generation Method and Causal Effect Heterogeneous Response Difference Estimation Method Based on Meta-Learner (基于元学习者的训练数据生成方法及因果效应异质反应差异估计方法). Fan Zhou, Chengtai Cao, Ting Zhong, and Xovee Xu
• China Invention Patent, No. CN114036398B: Content Recommendation and Ranking Model Training Method, Device, Equipment and Storage Medium (内容推荐和排序模型训练方法、装置、设备以及存储介质). Xiaoling Xia, Wei Xu, Chengtai Cao, Kunbin Chen, Bolei He, and Wei He
• China Invention Patent, No. CN114428887B: Click Data Denoising Method, Device, Electronic Device and Storage Medium (点击数据去噪方法、装置、电子设备及存储介质). Wei Xu, Xiaoling Xia, Junxiang Jiang, Chengtai Cao, Bolei He, Kunbin Chen, and Wei He
• USA Invention Patent, No. US12174824B2: Method for Denoising Click Data, Electronic Device and Storage Medium. Wei Xu, Xiaoling Xia, Junxiang Jiang, Chengtai Cao, Bolei He, Kunbin Chen, and Wei He
• Taiwan (China) Invention Patent, No. Method and Device for Calibrating Prediction Uncertainty (預測的不確定校正方法及校正裝置). Chengtai Cao, Jianping Wang, Yirong Lin, Nianyi Zhan, Yonghui Li, and Kailin Yang

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