Publications
2024
He Cheng, Depeng Xu, Shuhan Yuan, and Xintao Wu. “Achieving Counterfactual Explanation for Sequence Anomaly Detection”. In the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2024.
Xingyi Zhao, Depeng Xu, and Shuhan Yuan. “Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization”. In the Proceedings of the 41st International Conference on Machine Learning (ICML), 2024. link
Farsheed Haque, Depeng Xu, and Shuhan Yuan. “Discovering and Mitigating Indirect Bias in Attention-Based Model Explanations”. In the Findings of 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024. link
He Cheng and Shuhan Yuan. “Backdoor Attack against One-Class Sequential Anomaly Detection Models”. In the Proceedings of the 2024 Pacific-Asia International Conference on Knowledge Discovery and Data Mining (PAKDD), 2024. [arxiv] [code]
Vinay M.S., Shuhan Yuan, and Xintao Wu. “Contrastive Learning for Fraud Detection from Noisy Labels”. In the Proceedings of the 40th IEEE International Conference on Data Engineering (ICDE), 2024. link
2023
Vinay M.S., Shuhan Yuan, and Xintao Wu. “Robust Fraud Detection via Supervised Contrastive Learning.” In the Proceedings of the 2023 IEEE International Conference on Big Data (BigData), 2023. [arxiv]
Xiao Han, Shuhan Yuan, and Mohamed Trabelsi. “LogGPT: Log Anomaly Detection via GPT.” In the Proceedings of the 2023 IEEE International Conference on Big Data (BigData), 2023. (short paper) [arxiv] [code]
Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan. “On Root Cause Localization and Anomaly Mitigation through Causal Inference.” In the Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023. [arxiv] [code]
He Cheng, Depeng Xu, and Shuhan Yuan. “Explainable Sequential Anomaly Detection via Prototypes.” In the Proceedings of the 2023 International Conference on Neural Networks (IJCNN), 2023. [link] [code]
Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan. “Achieving Counterfactual Fairness for Anomaly Detection.” In the Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2023. [arxiv] [code]
2022
Vinay M.S., Shuhan Yuan, and Xintao Wu. “Fraud Detection via Contrastive Positive Unlabeled Learning.” In the Proceedings of the 2022 IEEE International Conference on Big Data (BigData), 2022. [link]
He Cheng, Depeng Xu, and Shuhan Yuan. “Sequential Anomaly Detection with Local and Global Explanations.” In the Proceedings of the 2022 IEEE International Conference on Big Data (BigData), 2022. [link] [code]
Xiao Han, Depeng Xu, Shuhan Yuan, and Xintao Wu. “Few-shot Anomaly Detection and Classification Through Reinforced Data Selection.” In the Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM), 2022. [link] [code]
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), Short Paper Track, 2022. [arxiv] [code]
Kshitiz Tiwari, Shuhan Yuan, and Lu Zhang. “Robust Hate Speech Detection via Mitigating Spurious Correlations.” In the Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (AACL-IJCNLP), Short Paper Track, 2022.
Vinay M.S., Shuhan Yuan and Xintao Wu. “Contrastive Learning for Insider Threat Detection.” In the Proceedings of the 27th International Conference on Database Systems for Advanced Applications (DASFAA), 2022. (short paper). [link]
Depeng Xu, Shuhan Yuan, Yueyang Wang, Angela Uchechukwu Nwude, Lu Zhang, Anna Zajicek, and Xintao Wu. “Coded Hate Speech Detection via Contextual Information.” In the Proceedings of 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2022. [link]
Panpan Zheng, Shuhan Yuan, and Xintao Wu. “Using Dirichlet Marked Hawkes Processes for Insider Threat Detection.” Digital Threats: Research and Practice 3, 1, Article 5 (March 2022), 19 pages. [link] [code]
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. [link]
2021
Xiao Han, He Cheng, Depeng Xu, and Shuhan Yuan. “InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data.” In the Proceedings of 2021 IEEE International Conference on Big Data (BigData), 2021. [link] [code] [PDF]
Panpan Zheng, Shuhan Yuan, Xintao Wu, and Yubao Wu. “Hidden Buyer Identification in Darknet Markets via Dirichlet Hawkes Process.” In the Proceedings of 2021 IEEE International Conference on Big Data (BigData), 2021. [arxiv]
Depeng Xu, Shuhan Yuan, and Xintao Wu. “Achieving Differential Privacy in Vertically Partitioned Multiparty Learning.” In the Proceedings of the 2021 IEEE International Conference on Big Data (BigData), 2021. (special session of privacy and security of big data) [arxiv]
Xiao Han, Shuhan Yuan. “Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation.” In the Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021. (short paper) [link] [code]
Jianting Chen, Shuhan Yuan, Dongdong Lv, and Yang Xiang. “A Novel Self-learning Feature Selection Approach Based on Feature Attributions.” Expert Systems with Applications, 2021. [link]
Haixuan Guo, Shuhan Yuan and Xintao Wu. “LogBERT: Log Anomaly Detection via BERT.” In the Proceedings of the International Conference on Neural Networks (IJCNN), 2021. [arxiv] [code]
Shuhan Yuan and Xintao Wu. “Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities.” Computer & Security, 2021. [link] [arxiv]
2020
Shuhan Yuan, Panpan Zheng, Xintao Wu, and Hanghang Tong. “Few-shot Insider Threat Detection.” In the Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 2020. (short paper) [link]
Molla Hafizur Rahman, Shuhan Yuan, Charles Xie, and Zhenghui Sha. “Predicting human design decisions with deep recurrent neural network combining static and dynamic data.” Design Science 6, 2020.
2019
Shuhan Yuan, Panpan Zheng, Xintao Wu and Qinghua Li. “Insider Threat Detection via Hierarchical Neural Temporal Point Processes.” In the Proceedings of 2019 IEEE International Conference on Big Data (BigData), 2019. [arxiv]
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu. “FairGAN+: Achieving Fair Data Generation and Fair Classification through Generative Adversarial Networks.” In the Proceedings of 2019 IEEE International Conference on Big Data (BigData), 2019.
Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu. “Achieving Causal Fairness through Generative Adversarial Networks.” In the Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI, 2019. [link]
Panpan Zheng, Shuhan Yuan, Xintao Wu. “SAFE: A Neural Survival Analysis Model for Fraud Early Detection.” In the Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI, 2019. [arxiv]
Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu. “One-Class Adversarial Nets for Fraud Detection.” In the Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI, 2019. [arxiv]
Yuemeng Li, Aidong Lu, Xintao Wu, Shuhan Yuan. “Dynamic Anomaly Detection Using Vector Autoregressive Model.” In the Proceedings of 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2019.
Depeng Xu, Shuhan Yuan, Xintao Wu. “Achieving Differential Privacy and Fairness in Logistic Regression.” In the Proceedings of Companion Proceedings of The 2019 World Wide Web Conference, 2019. [link] [PDF]
2018
Depeng Xu, Shuhan Yuan, Lu Zhang, Xintao Wu. “FairGAN: Fairness-aware Generative Adversarial Networks.” In the Proceedings of 2018 IEEE International Conference on Big Data (Big Data), 2018. [arxiv]
Shuhan Yuan, Xintao Wu, Yang Xiang. “Task-specific word identification from short texts using a convolutional neural network.” Intelligent Data Analysis, 2018. [arxiv]
Yuemeng Li, Shuhan Yuan, Xintao Wu, Aidong Lu. “On spectral analysis of directed signed graphs.” International Journal of Data Science and Analytics, 2018.
Depeng Xu, Shuhan Yuan, Xintao Wu, HaiNhat Phan. “DPNE: Differentially Private Network Embedding.” In the Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018. [PDF]
2017
Depeng Xu, Shuhan Yuan, Xintao Wu. “Differential Privacy Preserving Causal Graph Discovery.” In the Proceedings of 2017 IEEE Symposium on Privacy-Aware Computing (PAC), 2017. [PDF]
Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu. “Spectrum-based Deep Neural Networks for Fraud Detection.” In the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017. [PDF]
Shuhan Yuan, Xintao Wu, Yang Xiang, “SNE: Signed Network Embedding.” In the Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2017. [PDF] [Code]
2016
Shuhan Yuan, Xintao Wu, Yang Xiang, “Incorporating Pre-Training in Long Short-Term Memory Networks for Tweets Classification.” In the Proceedings of 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016. [PDF]
Shuhan Yuan, Xintao Wu, Yang Xiang. “A Two Phase Deep Learning Model for Identifying Discrimination from Tweets.” In the Proceedings of the 19th International Conference on Extending Database Technology (EDBT), 2016. [PDF]