Depeng Xu's Publications

DBLP | Google Scholar

• He Cheng, Depeng Xu, Shuhan Yuan, and Xintao Wu. Achieving Counterfactual Explanation for Sequence Anomaly Detection. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2024.
• Depeng Xu, Weichao Wang, and Aidong Lu. A Review of Motion Data Privacy in Virtual Reality. Proceedings of the 1st IEEE International Conference on Meta Computing (IEEE ICMC), 2024.
• Xingyi Zhao, Depeng Xu, and Shuhan Yuan. Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization. Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.
• Farsheed Haque, Depeng Xu, and Shuhan Yuan. Discovering and Mitigating Indirect Bias in Attention-Based Model Explanations. Findings of the Association for Computational Linguistics: NAACL 2024. paper
• Shuhan Yuan, Depeng Xu, and Xintao Wu. Trustworthy Anomaly Detection. SIAM International Conference on Data Mining (SDM) Tutorial, Houston, Texas, April 2024. link
• Xi Niu, Ruhani Rahman, Xiangcheng Wu, Zhe Fu, Depeng Xu, Riyi Qiu. Leveraging Uncertainty Quantification for Reducing Data for Recommender Systems. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), 2023.
• Thomas Carr, Aidong Lu and Depeng Xu. Linkage Attack on Skeleton-based Motion Visualization. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023. code
• He Cheng, Depeng Xu and Shuhan Yuan. Explainable Sequential Anomaly Detection via Prototypes. Proceedings of the 2023 International Joint Conference on Neural Networks (IJCNN), 2023.
• He Cheng, Depeng Xu, and Shuhan Yuan. Sequential Anomaly Detection with Local and Global Explanations. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), 2022.
• Xiao Han, Depeng Xu, Shuhan Yuan and Xintao Wu. Few-shot Anomaly Detection, and Classification Through Reinforced Data Selection. Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM), 2022.
• Xingyi Zhao, Lu Zhang, Depeng Xu, and Shuhan Yuan. Generating Textual Adversaries with Minimal Perturbation. In the Findings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
• Depeng Xu, Shuhan Yuan, Yueyang Wang, Angela Uchechukwu Nwude, Anna Zajicek and Xintao Wu. Coded Hate Speech Detection via Contextual Information. Proceedings of 26th Pacific-Asia Conference on Knowledge Discovery, and Data Mining (PAKDD), 2022.
• Xintao Wu, Depeng Xu, Shuhan Yuan and Lu Zhang. Fair Data Generation, and Machine Learning through Generative Adversarial Networks. Book chapter in Generative Adversarial Learning: Architectures, and Applications edited by Roozbeh Razavi-Far, Ariel Ruiz-Garcia, Vasile Palade, and Juergen Schmidhuber, ISBN 978-3-030-91389-2, 2022.
• Depeng Xu, Shuhan Yuan and Xintao Wu. Achieving Differential Privacy in Vertically Partitioned Multiparty Learning. Proceedings of the 2021 IEEE International Conference on Big Data (BigData), 2021.
• Xiao Han, He Cheng, Depeng Xu and Shuhan Yuan. InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data. Proceedings of the 2021 IEEE International Conference on Big Data (BigData), 2021.
• Depeng Xu, Wei Du and Xintao Wu. Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy. Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery, and Data Mining (KDD), 2021. paper code
• Wei Du, Depeng Xu, Xintao Wu and Hanghang Tong. Fairness-aware Agnostic Federated Learning. Proceedings of SIAM International Conference on Data Mining (SDM), 2021.
• Depeng Xu, Shuhan Yuan and Xintao Wu. Achieving Differential Privacy in Vertically Partitioned Multiparty Learning. International Workshop on Federated Learning for User Privacy, and Data Confidentiality in Conjunction with IJCAI 2020 (FL-IJCAI), 2020.
• Depeng Xu, Shuhan Yuan, Lu Zhang and Xintao Wu. FairGAN+: Achieving Fair Data Generation, and Classification through Generative Adversarial Nets. Proceedings of 2019 IEEE International Conference on Big Data (BigData), 2019. paper code
• Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang and Xintao Wu. Achieving Causal Fairness through Generative Adversarial Networks. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. paper code
• Depeng Xu, Shuhan Yuan, Lu Zhang and Xintao Wu. FairGAN+: Achieving Fair Data Generation, and Fair Classification through Generative Adversarial Networks. Informal Proceedings of the KDD 2019 Workshop on Explainable AI for Fairness, Accountability & Transparency (XAI), 2019.
• Depeng Xu, Shuhan Yuan and Xintao Wu. Achieving Differential Privacy, and Fairness in Logistic Regression. Proceedings of the WWW Workshop on Fairness, Accountability, Transparency, Ethics, and Society (FATES) on the Web, 2019.
• Depeng Xu, Shuhan Yuan, Lu Zhang and Xintao Wu. FairGAN: Fairness-aware Generative Adversarial Networks. Proceedings of the IEEE Big Data (BigData), 2018. paper code
• Depeng Xu, Shuhan Yuan, Xintao Wu and HaiNhat Phan. DPNE: Differentially Private Network Embedding. Proceedings of the Pacific-Asia Conference on Knowledge Discovery, and Data Mining (PAKDD), 2018.
• Depeng Xu, Shuhan Yuan and Xintao Wu. Differential Privacy Preserving Causal Graph Discovery. Proceedings of the 1st IEEE Symposium on Privacy-Aware Computing (PAC), 2017.
• Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan and Xintao Wu. DPWeka: Achieving Differential Privacy in WEKA. Proceedings of the 1st IEEE Symposium on Privacy-Aware Computing (PAC), 2017.