| 2026 | ACL | EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks. | Yijie Lu, Manman Zhao, Tianjie Ju, Zihe Yan, Xinbei Ma, Yuan Guo, Daizong Ding, Gongshen Liu, Zhuosheng Zhang |
| 2025 | EMNLP | Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents. | Pengzhou Cheng, Haowen Hu, Zheng Wu, Zongru Wu, Tianjie Ju, Daizong Ding, Zhuosheng Zhang, Gongshen Liu |
| 2024 | CVPR | CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification. | Yuanmin Huang, Mi Zhang, Daizong Ding, Erling Jiang, Zhaoxiang Wang, Min Yang |
| 2024 | ESORICS | Towards Detection-Recovery Strategy for Robust Decentralized Matrix Factorization. | Yuanmin Huang, Mi Zhang, Daizong Ding, Erling Jiang, Qifan Xiao, Xiaoyu You, Yuan Tian, Min Yang |
| 2024 | WWW | Uplift Modeling for Target User Attacks on Recommender Systems. | Wenjie Wang, Changsheng Wang, Fuli Feng, Wentao Shi, Daizong Ding, Tat-Seng Chua |
| 2023 | AAAI | Black-Box Adversarial Attack on Time Series Classification. | Daizong Ding, Mi Zhang, Fuli Feng, Yuanmin Huang, Erling Jiang, Min Yang |
| 2023 | CVPR | CAP: Robust Point Cloud Classification via Semantic and Structural Modeling. | Daizong Ding, Erling Jiang, Yuanmin Huang, Mi Zhang, Wenxuan Li, Min Yang |
| 2023 | WWW | Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model. | Xiaoyu You, Chi Li, Daizong Ding, Mi Zhang, Fuli Feng, Xudong Pan, Min Yang |
| 2023 | WWW | MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding. | Xiaoyu You, Beina Sheng, Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Fuli Feng |
| 2022 | ICDE | Towards Backdoor Attack on Deep Learning based Time Series Classification. | Daizong Ding, Mi Zhang, Yuanmin Huang, Xudong Pan, Fuli Feng, Erling Jiang, Min Yang |
| 2021 | CIKM | A Deep Learning Framework for Self-evolving Hierarchical Community Detection. | Daizong Ding, Mi Zhang, Hanrui Wang, Xudong Pan, Min Yang, Xiangnan He |
| 2021 | CIKM | Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction. | Xiaoyu You, Mi Zhang, Daizong Ding, Fuli Feng, Yuanmin Huang |
| 2020 | AAAI | Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning. | Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He |
| 2020 | CCS | Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware. | Xiaohan Zhang, Yuan Zhang, Ming Zhong, Daizong Ding, Yinzhi Cao, Yukun Zhang, Mi Zhang, Min Yang |
| 2020 | ICDM | Modeling Personalized Out-of-Town Distances in Location Recommendation. | Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He |
| 2019 | KDD | Modeling Extreme Events in Time Series Prediction. | Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He |
| 2018 | ICML | Theoretical Analysis of Image-to-Image Translation with Adversarial Learning. | Xudong Pan, Mi Zhang, Daizong Ding |
| 2018 | WWW | Geographical Feature Extraction for Entities in Location-based Social Networks. | Daizong Ding, Mi Zhang, Xudong Pan, Duocai Wu, Pearl Pu |
| 2017 | CIKM | BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network. | Daizong Ding, Mi Zhang, Shao-Yuan Li, Jie Tang, Xiaotie Chen, Zhi-Hua Zhou |