Ding, Kaize

27 publications

ICLR 2026 G-Merging: Graph Models Merging for Parameter-Efficient Multi-Task Knowledge Consolidation Jun Chen, Ziyue Qiao, Qin Zhang, Kaize Ding, Xiao Luo

ICLR 2026 GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback Ruiyao Xu, Kaize Ding

ICLR 2026 Task Vectors, Learned Not Extracted: Performance Gains and Mechanistic Insights Haolin Yang, Hakaze Cho, Kaize Ding, Naoya Inoue

ECML-PKDD 2025 Cross-Domain Conditional Diffusion Models for Time Series Imputation Kexin Zhang, Baoyu Jing, K. Selçuk Candan, Dawei Zhou, Qingsong Wen, Han Liu, Kaize Ding

NeurIPS 2025 Glocal Information Bottleneck for Time Series Imputation Jie Yang, Kexin Zhang, Guibin Zhang, Philip S. Yu, Kaize Ding

TMLR 2025 LEGO-Learn: Label-Efficient Graph Open-Set Learning Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Mengyuan Li, Kaize Ding, Yue Zhao

ICLR 2025 On Large Language Model Continual Unlearning Chongyang Gao, Lixu Wang, Kaize Ding, Chenkai Weng, Xiao Wang, Qi Zhu

NeurIPS 2025 Pareto-Optimal Energy Alignment for Designing Nature-like Antibodies Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu, Kaize Ding, Han Liu

NeurIPS 2025 Topology-Aware Conformal Prediction for Stream Networks Jifan Zhang, Fangxin Wang, Zihe Song, Philip S. Yu, Kaize Ding, Shixiang Zhu

ICLR 2025 Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Rui Miao, Kaize Ding, Ying Wang, Shirui Pan, Xin Wang

AAAI 2024 Data-Efficient Graph Learning Kaize Ding

WACV 2024 MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders Zhangsihao Yang, Kaize Ding, Huan Liu, Yalin Wang

NeurIPS 2024 Revisiting Score Propagation in Graph Out-of-Distribution Detection Longfei Ma, Yiyou Sun, Kaize Ding, Zemin Liu, Fei Wu

AAAI 2024 Sterling: Synergistic Representation Learning on Bipartite Graphs Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong

AAAI 2023 Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu

NeurIPS 2023 Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation Zhangsihao Yang, Mengwei Ren, Kaize Ding, Guido Gerig, Yalin Wang

NeurIPSW 2023 Topological and Temporal Data Augmentation for Temporal Graph Networks Haoran Liu, Jianling Wang, Kaize Ding, James Caverlee

NeurIPS 2023 Towards Self-Interpretable Graph-Level Anomaly Detection Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

NeurIPSW 2023 Uncertainty-Aware Robust Learning on Noisy Graphs Shuyi Chen, Kaize Ding, Shixiang Zhu

NeurIPS 2022 BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H Chen, Zhihao Jia, Philip S Yu

IJCAI 2022 Few-Shot Learning on Graphs Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

AAAI 2022 Meta Propagation Networks for Graph Few-Shot Semi-Supervised Learning Kaize Ding, Jianling Wang, James Caverlee, Huan Liu

ECML-PKDD 2022 Supervised Graph Contrastive Learning for Few-Shot Node Classification Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

LoG 2022 Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu

AAAI 2021 Fact-Enhanced Synthetic News Generation Kai Shu, Yichuan Li, Kaize Ding, Huan Liu

IJCAI 2020 Inductive Anomaly Detection on Attributed Networks Kaize Ding, Jundong Li, Nitin Agarwal, Huan Liu

IJCAI 2019 InterSpot: Interactive Spammer Detection in Social Media Kaize Ding, Jundong Li, Shivam Dhar, Shreyash Devan, Huan Liu