Publications

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Conferences: KDD(9), IJCAI(11), AAAI(5), NeurIPS(2), WWW(2), ICDE(2), ICDM(7), SDM(7), CIKM(3), ECML-PKDD(4), ECAI(1), ASE(1), BIBM(4),  DASFAA(2), PAKDD(13), BigData(22); Journals: TKDE (3), TKDD(2), TCBB(1), TOSEM(1), TDSC(2), TMLR(1), KAIS(4), JIIS(4), ML(1)

2024

  • Huy Mai and Xintao Wu. “Federated Learning under Sample Selection Heterogeneity”. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington DC, Dec 15-18, 2024. (short paper, acceptance ratio 124+130/660).
  • Alycia Carey, Karuna Bhaila, Kennedy Edemacu, and Xintao Wu. “DP-TabICL: In-Context Learning with Differentially Private Tabular Data”. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington DC, Dec 15-18, 2024. (short paper, acceptance ratio 124+130/660).
  • Prateek Verma, Minh-Hao Van, and Xintao Wu. “Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis”. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington DC, Dec 15-18, 2024. (short paper, acceptance ratio 124+130/660).
  • Qixin Wang, Xintao Wu, Kan Yao, and Han Li. ” A Content-Aware Deep Learning Recommendation System”. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington DC, Dec 15-18, 2024. (short paper, Industry and Government track).
  • Chen Zhao and Xintao Wu. “Supervised Algorithmic Fairness in Distribution Shifts”. 2024 IEEE International Conference on Big Data (BigData), Washington DC, Dec 15-18, 2024. (tutorial).
  • Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu. “Causal Diffusion Autoencoders: Toward Representation-Enabled Counterfactual Generation via Diffusion Probabilistic Models”. In Proceedings of the 27th European Conference on Artificial Intelligence (ECAI), Oct 19-24, 2024. (acceptance rate 547/2344)
  • He Cheng, Depeng Xu, Shuhan Yuan, Xintao Wu. “Achieving Counterfactual Explanation for Sequence Anomaly Detection”. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Vilnius, Lithuania, Sept 9-13, 2024. (acceptance rate 198/826)
  • Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Earl Grant, Feng Chen. “Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously”. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Barcelona, Spain, Aug 25-29, 2024. (acceptance rate ~20%)
  • Aneesh Komanduri, Xintao Wu, Yongkai Wu, and Feng Chen. “From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling”. Transactions on Machine Learning Research. May 2024. https://arxiv.org/pdf/2310.11011.pdf
  • Kennedy Edemacu and Xintao Wu. “Privacy Preserving Prompt Engineering: A Survey“. April 2024. https://arxiv.org/pdf/2404.06001.pdf
  • Aneesh Komanduri, Yongkai Wu, Feng Chen, and Xintao Wu. “Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms”. Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI), Jeju Island, South Korea, August 3-8, 2024.
  • Minglai Shao, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, and Qin Tian. “Supervised Algorithmic Fairness in Distribution Shifts: A Survey”. Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI), Jeju Island, South Korea, August 3-8, 2024.
  • Alycia Carey, Minh-Hao Van, and Xintao Wu. “Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions”. Proceedings of the IEEE International Joint Conference on Neural Network (IJCNN), Yokohama, Japan, June 30 – July 5, 2024.
  • Huy Mai, and Xintao Wu. “On Prediction Feature Assignment in the Heckman Selection Model”. Proceedings of the IEEE International Joint Conference on Neural Network (IJCNN), Yokohama, Japan, June 30 – July 5, 2024.
  • Karuna Bhaila, and Xintao Wu. “Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks”. Proceedings of the IEEE International Joint Conference on Neural Network (IJCNN), Yokohama, Japan, June 30 – July 5, 2024.
  • Minh-Hao Van, Prateek Verma, and Xintao Wu. “On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study”. Proceedings of the 9th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Wilmington, DE, June 19-21, 2024. (short paper)
  • Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu. “Causal Diffusion Autoencoders: Toward Representation-Enabled Counterfactual Generation via Diffusion Probabilistic Models”. CVPR Workshop on Generative Models for Computer Vision, June 18, 2024.
  • Vinay M.S., Shuhan Yuan, and Xintao Wu. “Contrastive Learning for Fraud Detection from Noisy Labels”. Proceedings of the 40th IEEE International Conference on Data Engineering (ICDE), Utrecht, Netherlands, May 13-16, 2024. (acceptance ratio 376/1481)
  • Minh-Hao Van, Alycia Carey, and Xintao Wu. “Robust Influence-based Training Methods for Noisy Brain MRI”. Proceedings of the 2024 Pacific-Asia International Conference on Knowledge Discovery and Data Mining (PAKDD), Taipei, May 7-10, 2024. (acceptance ratio 175/720)
  • Karuna Bhaila, Wen Huang, Yongkai Wu, and Xintao Wu. “Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach”. Proceedings of the 24th SIAM International Conference on Data Mining (SDM), Houston, Texas, April 18-20, 2024. (acceptance ratio 98/336).
  • Shuhan Yuan, Depeng Xu, and Xintao Wu. “Trustworthy Anomaly Detection”. SIAM International Conference on Data Mining (SDM), Houston, Texas, April 18-20, 2024. (tutorial).
  • Wen Huang and Xintao Wu. “Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach”. Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, Feb 20-27, 2024. (acceptance ratio 2342/9862).
  • Minh-Hao Van, and Xintao Wu. “Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model”. Informal Proceedings of the AAAI Workshop on Multimodal Fact Checking and Hate Speech Detection (DeFactify), Vancouver, Canada, Feb 26, 2024.
  • Karuna Bhaila, and Xintao Wu. “Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks”. Informal Proceedings of the 3rd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE) Vancouver, Canada, Feb 26, 2024.
  • Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, and Feng Chen. “Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness”. ACM Transactions on Knowledge Discovery from Data, 18(6):1-23, April 2024.

2023

  • Vinay M.S., Shuhan Yuan, and Xintao Wu. “Robust Fraud Detection via Supervised Contrastive Learning”. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, Dec 15-18, 2023. (regular paper, acceptance ratio 92/526).
  • Wen Huang, Jingbo Zhou, Xintao Wu, and Dejing Dou. “Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach”. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, Dec 15-18, 2023. (regular paper, acceptance ratio 92/526).
  • Huy Mai, Wen Huang, Wei Du, and Xintao Wu. “A Robust Classifier under Missing-Not-at-Random Sample Selection Bias”. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, Dec 15-18, 2023. (short paper, acceptance ratio 92+111/526).
  • Alycia Carey, Karuna Bhaila, and Xintao Wu. “Randomized Response Has No Disparate Impact on Model Accuracy”. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, Dec 15-18, 2023. (special session of Privacy and Security of Big Data).
  • Karuna Bhaila, Wen Huang, Yongkai Wu, and Xintao Wu. “Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach”. NeurIPS Workshop on New Frontiers in Graph Learning, New Orleans, LA, Dec 10-16, 2023.
  • Aneesh Komanduri, Yongkai Wu, Feng Chen, and Xintao Wu. “Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms”. NeurIPS Workshop on Causal Representation Learning, New Orleans, LA, Dec 10-16, 2023.
  • Phung Lai, Hai Phan, Issa Khalil, Abdallah Khreishah, and Xintao Wu. “How to Backdoor HyperNetwork in Personalized Federated Learning?”. NeurIPS Workshop on Backdoors in Deep Learning – The Good, the Bad, and the Ugly, New Orleans, LA, Dec 10-16, 2023.
  • Minh-Hao Van, Alycia Carey, and Xintao Wu. “HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks”. Proceedings of the 2023 IEEE International Conference on Data Mining (ICDM), Shanghai, China, Dec 1-4, 2023. (regular paper, acceptance ratio 94/1003).
  • Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen. “Towards Fair Disentangled Online Learning for Changing Environments”. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Long Beach, CA, Aug 6 –10, 2023. (acceptance ratio 313/1416).
  • Vinay M.S., Shuhan Yuan, and Xintao Wu. “Robust Fraud Detection via Supervised Contrastive Learning”. The 3rd Workshop on Artificial Intelligence-Enabled Cybersecurity Analytics (AI4Cyber), Long Beach, CA, Aug 7, 2023.
  • Ethan Prihar, Aaron Haim,Tracy Jia Shen, Adam Sales, Dongwon Lee, Xintao Wu, Neil T. Heffernan. “Investigating the Impact of Skill-Related Videos on Online Learning”. Proceedings of the 10th ACM Conference on Learning at Scale, Copenhagen, Denmark, July 20-22, 2023.
  • Alycia N. Carey and Xintao Wu. “The Statistical Fairness Field Guide: Perspectives from Social and Formal Sciences”. AI and Ethics, 3(1):1-23, 2023.

2022

  • Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, and Xintao Wu. “SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Karuna Bhaila, Yongkai Wu, and Xintao Wu. “Fair Collective Classification in Networked Data”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Vinay M.S., Shuhan Yuan, and Xintao Wu. “Fraud Detection via Contrastive Positive Unlabeled Learning”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Alycia Carey, Wei Du, and Xintao Wu. “Robust Personalized Federated Learning under Demographic Fairness Heterogeneity”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Wei Du, Xintao Wu, and Hanghang Tong. “Fair Regression under Sample Selection Bias”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Minh-Hao Van, Wei Du, Xintao Wu, Feng Chen, and Aidong Lu. “Defending Evasion Attacks via Adversarially Adaptive Training”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, and Hanghang Tong. “InfoFair: Information-Theoretic Intersectional Fairness”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (regular paper, acceptance ratio 122/633).
  • Khang Tran, Phung Lai, Hai Phan, Issa Khalil, Yao Ma, Abdallah Khreishah, My Thai, and Xintao Wu. “Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks”. Proceedings of the 2022 IEEE International Conference on Big Data (BigData), Osaka, Japan, Dec 17-20, 2022. (short paper, acceptance ratio 122+118/633).
  • 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), Orlando, FL, Nov 28 – Dec 1, 2022. (short paper, acceptance ratio 85+89/885)
  • Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen”Adaptive Fairness-Aware Online Meta-Learning for Changing Environments”. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington, DC, Aug 14 –18, 2022. (acceptance ratio 254/1695).
  • Depeng Xu, Shuhan Yuan, Yueyang Wang, Angela Uchechukwu Nwude, Lu Zhang, Anna Zajicek and Xintao Wu. “Coded Hate Speech Detection via Contextual Information”. Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Chengdu, China, May 16 –19, 2022. (acceptance ratio 121/627).
  • Minh-Hao Van, Wei Du, Xintao Wu and Aidong Lu. “Poisoning Attacks on Fair Machine Learning”. Proceedings of the 27th International Conference on Database Systems for Advanced Applications (DASFAA), Hyderabad, India, April 11 –14, 2022. (full paper).
  • S. Vinay, Shuhan Yuan and Xintao Wu. “Contrastive Learning for Insider Threat Detection”. Proceedings of the 27th International Conference on Database Systems for Advanced Applications (DASFAA), Hyderabad, India, April 11 –14, 2022. (short paper).
  • Wen Huang, Lu Zhang, and Xintao Wu. “Achieving Counterfactual Fairness for Causal Bandit”. Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, Feb 22 –March 1, 2022. (acceptance ratio 1349/9251).
  • Wen Huang, Kevin Labille, Xintao Wu, Dongwon Lee and Neil Heffernan. “Achieving User-Side Fairness in Contextual Bandits”. Human-Centric Intelligent Systems, Springer, 2022. link
  • Alycia N. Carey and Xintao Wu. “The Causal Fairness Field Guide: Perspectives from Social and Formal Sciences”. Frontiers Big Data 5: 892837, 2022. link
  • 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

  • Panpan Zheng, Shuhan Yuan, Xintao Wu, and Yubao Wu. “Hidden Buyer Identification in Darknet Markets via Dirichlet Hawkes Process”. Proceedings of the 2021 IEEE International Conference on Big Data (BigData), Dec 15-18, 2021. (regular paper, acceptance ratio 97/486)
  • Wen Huang, Kevin Labille, Xintao Wu, Dongwon Lee, and Neil Heffernan. “Fairness-aware Bandit-based Recommendation”. Proceedings of the 2021 IEEE International Conference on Big Data (BigData), Dec 15-18, 2021. (short paper, acceptance ratio 97+96/486)
  • 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), Dec 15-18, 2021. (special session of privacy and security of big data)
  • Wen Huang, Lu Zhang, and Xintao Wu. “Achieving Counterfactual Fairness for Causal Bandit”. NeurIPS2021 workshop, Algorithmic Fairness through the Lens of Causality and Robustness (AFCR), Dec 6-14, 2021.
  • Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil Heffernan, Xintao Wu, Ben Graff, Dongwon Lee. “MathBERT:A Pre-trained Language Model for General NLP Tasks in Mathematics Education”. Proceedings of the NeurIPS2021 workshop, Math AI for Education: Bridging the Gap Between Research and Smart Education (Math4ED), Dec 6-14, 2021. Best Paper Award
  • Wei Du and Xintao Wu. “Fair and Robust Classification Under Sample Selection Bias”. Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), Nov 1-5, 2021. (short paper, acceptance ratio 177/626)
  • Depeng Xu, Wei Du and Xintao Wu. “Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent”. Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Aug 14-18, 2021. (acceptance ratio 238/1541)
  • Haixuan Guo, Shuhan Yuan and Xintao Wu. “LogBERT: Log Anomaly Detection via BERT”. Proceedings of the International Conference on Neural Networks (IJCNN), July 18-22, 2021.
  • Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil Heffernan, Xintao Wu, Sean McGrew and Dongwon Lee. “Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT”.  Proceedings of 22nd International Conference on Artificial Intelligence in Education (AIED), June 14-18, 2021. (acceptance ratio 40/168).
  • Chenyi Hu, Victor Sheng, Ningning Wu, and Xintao Wu. “Managing Uncertainty in Crowdsourcing with Interval-valued Labels”.  Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), June 7-9, 2021.
  • Labille, W. Huang, and X. Wu. “Transferable Contextual Bandits with Prior Observations”.  Proceedings of 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 11-14, 2021. (acceptance ratio 157/768).
  • Zhou, L. Li, X. Wu, N. Cao, L. Ying, and H. Tong. “ATTENT: Active Attributed Network Alignment”.  Proceedings of the WEB Conference (formerly WWW), May 13-17, 2021. (acceptance ratio 357/1736)
  • Du, D. Xu, X. Wu and H. Tong. “Fairness-aware Agnostic Federated  Learning”.  Proceedings of SIAM International Conference on Data Mining, April 29-May 1, 2021 (acceptance ratio 85/400).
  • Hu, Y. Wu, L. Zhang, and X. Wu. “A Generative Adversarial Framework for Bounding Confounded Causal Effects”.  Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), Feb 2-9, 2021. (acceptance ratio 1692/9034).
  • Feng, J. Li, L. Jiao and X. Wu. “Towards Learning-Based, Content-Agnostic Detection of Social Bot Traffic”.  IEEE Transactions on Dependable and Secure Computing, 2021.
  • Du and X. Wu. “Enhancing Personalized Modeling via Weighted and Adversarial Learning”.  International Journal of Data Science and Analytics, 2021. (invited extension from DSAA’20)
  • Yuan and X. Wu. “Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities “. Computer & Security, 104:102221, 2021.
  • Zheng, S. Yuan, and X. Wu. “Using Dirichlet Marked Hawkes Processes for Insider Threat Detection”. Digital Threats: Research and Practice, 2021, ACM.
  • Hu, C. Hu, Y. Fan and X. Wu. “oGBAC–A Group Based Access Control Framework for Information Sharing in Online Social Networks”. IEEE Transactions on Dependable and Secure Computing, 18(1):100-116, 2021.

 

2020

  • Hu, Y. Wu, L. Zhang, and X. Wu. “Fair Multiple Decision Making through Soft Interventions”. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), Dec 6-12, 2020 (acceptance ratio 1900/9454).
  • Yuan, P. Zheng, X. Wu, and H. Tong. “Few-shot Insider Threat Detection”. Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Oct 19-23, 2020 (short paper, acceptance ratio 103/397).
  • Du and X. Wu. “AdvPL: Adversarial Personalized Learning”. Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, Sydney, Australia, Oct 6-9, 2020.
  • Shao, Z. Qiu, X. Yu, G. Jin, T. Xie and X. Wu. “Database-Access Performance Antipatterns in Database-Backed Web Applications”. Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, Sept 27 – Oct 3, 2020 (acceptance ratio 50/201).
  • Xu, S. Yuan, and X. 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), Yokohama, Japan, 2020.
  • Huang, Y.Wu, and X. Wu. “Multi-cause Discrimination Analysis Using Potential Outcomes”. Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC, Oct 18-21, 2020.
  • Huang, Y.Wu, L. Zhang, and X. Wu. “Fairness through Equality of Effort”. Proceedings of the WWW Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web, Taipei, April 21, 2020.

 

2019

  • Y. Wu, L. Zhang, X. Wu, and H. Tong.  “PC-Fairness: A Unified Framework for Measuring Causality-based Fairness”. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec 8-14, 2019. (acceptance ratio 1428/6743)
  • S. Yuan, P. Zheng, X. Wu and Q. Li.  “Insider Threat Detection via Hierarchical Neural Temporal Point Processes”. Proceedings of 2019 IEEE International Conference on Big Data (BigData), Los Angeles, CA, USA, December 9-12, 2019. (acceptance ratio 106/550)
  • D. Xu, S. Yuan, L. Zhang and X. Wu.  “FairGAN+: Achieving Fair Data Generation and Fair Classification through Generative Adversarial Networks”. Proceedings of 2019 IEEE International Conference on Big Data (BigData), Los Angeles, CA, USA, December 9-12, 2019. (acceptance ratio 106+105/550)
  • D. Xu, Y. Wu, S. Yuan, L. Zhang and X. Wu.  “Achieving Causal Fairness through Generative Adversarial Networks”. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 10-16, 2019. (acceptance ratio 850/4752)
  • Y. Wu, L. Zhang and X. Wu.  “Counterfactual Fairness: Unidentification, Bound and Algorithm”. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 10-16, 2019. (acceptance ratio 850/4752)
  • N. Phan, M. Vu, Y. Liu, R. Jin, X. Wu, D. Dou and M. Thai.  “Heterogeneous Gaussain Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness”. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 10-16, 2019. (acceptance ratio 850/4752)
  • D. Xu, S. Yuan, L. Zhang and X. 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), Anchorage, Alaska, August 4-8, 2019.
  • Y. Feng, J. Li, L. Jiao and X. Wu. “BotFlowMon: Learning-Based, Context-Agnostic Identification of Social Bot Traffic Flows”.  Proceedings of the 7th IEEE Conference on Communications and Network Security (CNS), Washington DC, June 10-12, 2019. (acceptance ratio 32/115)  Best Paper Award
  • Y. Wu, L. Zhang and X. Wu. “On Convexity and Bounds of Fairness-aware Classification”.  Proceedings of the WEB Conference (formerly WWW), San Francisco, California, May 13-17, 2019. (short paper, acceptance ratio 72/361)
  • D. Xu, S. Yuan and X. Wu.  “Achieving Differential Privacy and Fairness in Logistic Regression”. Proceedings of the WWW Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web, San Francisco, California, May 14, 2019.
  • Y. Li, A. Lu, X. Wu and S. Yuan. “Dynamic Anomaly Detection using Vector Autoregressive Model”.  Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Macau, China, April 14-17, 2019. (acceptance ratio 140/567)
  • P. Zheng, S. Yuan and X. Wu. “SAFE: A Neural Survival Analysis Model for Fraud Early Detection”.  Proceedings of the  33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, Jan 27- Feb 1, 2019. (acceptance ratio 1150/7095)
  • P. Zheng, S. Yuan, X. Wu, J. Li and A. Lu. “One-Class Adversarial Nets for Fraud Detection”.  Proceedings of the  33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, Jan 27- Feb 1, 2019. (acceptance ratio 1150/7095)
  • L. Zhang, Y. Wu and X. Wu. “Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms”.  IEEE Transactions on Knowledge Discovery and Data Engineering, 31(11):2035-2050, Nov 2019.  DOI  10.1109/TKDE.2018.2872988
  • L. Zhang, Q. Pan, Y. Wang, X. Wu, and X. Shi. “Bayesian Network Construction and Genotype-Phenotype Inference Using GWAS Statistics”.  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(2):475-489, March-April 2019.

 

2018

  • D. Xu, S. Yuan, L. Zhang and X. Wu. “FairGAN: Fairness-aware Generative Adversarial Networks”.  Proceedings of the IEEE Big Data, Seattle, WA,  USA, Dec 10- 13, 2018. (acceptance ratio 98+103/518)
  • Y. Wu, L. Zhang and X. Wu. “On Discrimination Discovery and Removal in Ranked Data using Causal Graph”.  Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), London, United Kingdom, August 19-23, 2018. (acceptance ratio 107+74/983)
  • L. Zhang, Y. Wu and X. Wu. “Achieving Non-Discrimination in Prediction”.  Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 13-19, 2018. (acceptance ratio 710/3470)
  • D. Xu, S. Yuan, X. Wu and N. Phan. “DPNE: Differentially Private Network Embedding”.  Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Melbourne, Australia, June 3-6, 2018. (regular paper, 57+107/592)
  • S. Yuan, X. Wu and Y. Xiao. “Incorporating Pre-Training in Long Short-Term Memory Networks for Tweets Classification”.  Social Network Analysis and Mining, 8(1):52:1-16, 2018.
  • Y. Li, S. Yuan, X. Wu and A. Lu. “On Spectral Analysis of Directed Signed Graphs”.  International Journal of Data Science and Analytics, 6(2):147-162, 2018.
  • S. Yuan, X. Wu and Y. Xiang. “Task-specific Word Identification from Short Texts Using a Convolutional Neural Network”.  Intelligent Data Analysis, 22(3):533-550, 2018.

2017

  • N. Phan, X. Wu, H. Hu and D. Dou. “Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning”.  Proceedings of the IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA, Nov 18-21, 2017. (regular paper, acceptance ratio 72/778) updated arXiv version
  • L. Zhang, Q. Pan, and X. Wu. “Modeling SNP and Quantitative Trait Association from GWAS Catalog Using CLG Bayesian Network”.  Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, Nov 13-16, 2017. (short paper, acceptance ratio 79+81/414)
  • Q. Pan, L. Zhang, and X. Wu. “STIP: An SNP-Trait Inference Platform”.  Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, Nov 13-16, 2017. (industry track)
  • S. Yuan, X. Wu, A. Lu and J. Li. “Spectrum-based Deep Neural Networks for Fraud Detection”.  Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM), Singapore, Nov 6-10, 2017. (short paper, acceptance ratio 119/398) arXiv version
  • Y. Li, X. Wu and A. Lu. “On Spectral Analysis of Directed Signed Graphs”.  Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, Oct 19-21, 2017. (acceptance ratio 25%) arXiv version
  • N. Phan, X. Wu and D. Dou. “Preserving Differential Privacy in Convolutional Deep Belief Networks”.  Machine Learning, 106(9-10):1681-1704, 2017. (special issue of ECML/PKDD journal track) updated arXiv version
  • S. Yuan, P. Zheng, X. Wu and Y. Xiang. “Wikipedia Vandal Early Detection: from User Behavior to User Embedding”.  Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Skopje, Macedonia, Sept 18-22, 2017. (acceptance ratio 104/384) arXiv version
  • L. Zhang, Y. Wu and X. Wu. “A Causal Framework for Discovering and Removing Direct and Indirect Discrimination”.  Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, August 19-25, 2017. (acceptance ratio 660/2540)
  • L. Zhang, Y. Wu and X. Wu. “Achieving Non-Discrimination in Data Release”.  Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Halifax, Nov Scotia, Canada, August 13-17, 2017. (acceptance ratio 64+67/748)
  • D. Xu, S. Yuan and X. Wu. “Differential Privacy Preserving Causal Graph Discovery”.  Proceedings of the 1st IEEE Symposium on Privacy-Aware Computing (PAC), Washington DC, USA, August 1-3, 2017. (acceptance ratio 15/36)
  • S. Katla, D. Xu, Y. Wu, Q. Pan and X. Wu. “DPWeka: Achieving Differential Privacy in WEKA”.  Proceedings of the 1st IEEE Symposium on Privacy-Aware Computing (PAC), Washington DC, USA, August 1-3, 2017. poster
  • S. Yuan, X. Wu and Y. Xiang. “SNE: Signed Network Embedding”.  Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Jeju, South Korea,  May 23-26, 2017. (acceptance ratio 45+84/458) arXiv version
  • L. Zhang and X. Wu. “Anti-discrimination Learning: A Causal Modeling-based Framework”.  International Journal of Data Science and Analytics, 4(1):1-16, 2017. (Invited position paper) online version
  • L. Wu, X. Wu, A. Lu and Y. Li. “On Spectral Analysis of Signed and Dispute Graphs: Application to Community Structure”.  IEEE Transactions on Knowledge Discovery and Data Engineering, 29(7):1480-1493, 2017.
  • X. Shi and X. Wu. “An Overview of Human Genetic Privacy”.  Annals of the New York Academy of Sciences, 1387(1)61-72, Jan 2017. link

2016

  • L. Zhang, Q. Pan, X. Wu and X. Shi. ” Building Bayesian Networks from GWAS Statistics Based on Independence of Causal Influence”.  Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China, Dec 15-18, 2016. (acceptance ratio 70+69/361)
  • S. Yuan, X. Wu and Y. Xiao. “Incorporating Pre-Training in Long Short-Term Memory Networks for Tweets Classification”.  Proceedings of the IEEE International Conference on Data Mining (ICDM), Barcelona, Spain, Dec 13-15, 2016. (acceptance ratio 8.5+11.1%)
  • D. Hu, X. Zhang, Y. Fan, Z. Zhao, L. Wang, X. Wu and X. Wu. “On Digital Image Trustworthiness”.  Applied Soft Computing, November, 2016. link
  • Y. Wu and X. Wu. “Using Loglinear Model for Discrimination Discovery and Prevention”.  Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, Canada, Oct 17-19, 2016. (acceptance ratio 21.4%)
  • Y. Li, X. Wu and S. Yang. “Social Network Dominance based on Analysis of Asymmetry”. Proceedings of the  IEEE/ACM  International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, USA, Aug 18-21, 2016.  (acceptance ratio 44+41/338)
  • Y. Wang, J. Wei, X. Wu and X. Shi. “Infringement of Individual Privacy via Mining Differentially Private GWAS Statistics”.  Proceedings of the 2nd International Conference on Big Data Computing and Communications (BIGCOM), Shenyang, China, July 29-31, 2016.
  • L. Zhang, Y. Wu and X. Wu. “Situation Testing-Based Discrimination Discovery: A Causal Inference Approach”. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), New York City, July 9-15, 2016. (acceptance rate <25%)
  • L. Zhang, Y. Wu and X. Wu. “On Discrimination Discovery Using Causal Networks”.  Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRimS), Washington DC, June 28-July 1, 2016
  • D. Hu, F. Chen, X. Wu and Z. Zhao. “A Framework of Privacy Decision Recommendation for Image Sharing in Online Social Networks”.  Proceedings of the 1st IEEE International Conference on Data Science in Cyberspace (DSC), Changsha, China, June 13-16, 2016. pdf
  • N. Phan, Y. Wang, X. Wu and D. Dou. “Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction”.  Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI),  Phoenix, Arizona, USA, Feb 12-17, 2016. (acceptance ratio 549/2132)
  • Y. Wang, X. Wu and D. Hu. “Using Randomized Response for Differential Privacy Preserving Data Collection”.  Proceedings of the 9th International Workshop on Privacy and Anonymity in the Information Society (PAIS),  Bordeaux, France, March 15, 2016.
  • S. Yuan, X. Wu and Y. Xiang. “A Two Phase Deep Learning Model for Identifying Discrimination from Tweets”.  Proceedings of the 19th International Conference on Extending Database Technology (EDBT),  Bordeaux, France, March 15-18, 2016.
  • X. Shi and X. Wu. “Genetic Privacy: Risks, Ethics, and Protection Techniques”.  Proceedings of the NSF Workshop on Data Science, Learning, and Applications to Biomedical & Health Sciences (DSLA-BHS),  New York City, NY, USA, Jan 7-8, 2016.

2015

  • Y. Li, X. Wu and A. Lu. “Analysis of Spectral Space Properties of Directed Graphs using Matrix Perturbation Theory with Application in Graph Partition”.  Proceedings of the IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, USA, Nov 14-17, 2015. (acceptance ratio 18.2%)
  • X. Hu, L. Wu, A. Lu and X. Wu.   “Block-Organizer Topology Visualization for Visual Exploration of Signed Networks”.  Proceedings of the IEEE ICDM Workshop on Data Mining Meets Visual Analytics at Big Data Era (DAVA’15), Atlantic City, NJ, USA, Nov 14, 2015.
  • Y. Wang, C. Si and X. Wu. “Regression Model Fitting under Differential Privacy and Model Inversion Attack”.  Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI),  Buenos Aires, Argentina, July 25-31, 2015. (acceptance ratio 575/1996)
  • Z. Luo, Y. Wang, X. Wu, W. Cai and T. Chen. “On Burst Detection and Prediction in Retweeting Sequence”.  Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD),  Ho Chi Minh, Vietnam, May 19-22, 2014. (acceptance ratio 90/405)
  • J-H. Hu, D-C. Zhan, X. Wu, Y.  Jiang, and Z-H. Zhou. “Pairwise Specific Distance Learning from Physical Linkages”.  ACM Transactions on Knowledge Discovery from Data, 9(3):20, 2015.
  • F. Luo and X. Wu. “Guest Editorial for Special Section on BIBM 2013”.  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(2):252-253, 2015.
  • D. Hu, B. Su, S. Zheng, Z. Zhao, X. Wu and X. Wu. “Security and Privacy Protocols for Perceptual Image Hashing”.  International Journal of Sensor Networks, 17(3):146-162, 2015.
  • K. Pan, X. Wu, and T. Xie. “Program-Input Generation for Testing Database Applications Using Existing Database States”.  Automated Software Engineering, 22(4):439-473, 2015.

2014

  • L. Wu, X. Wu, A.  Lu, and Y. Li. “On Spectral Analysis of Signed and Dispute Graphs”.  Proceedings of the IEEE International Conference on Data Mining,  Shenzhen, China, Dec 14-17, 2014. (short paper, acceptance ratio 71+71/727 )
  • K. Pan, X. Wu, and T. Xie. “Guided Test Generation for Database Applications via Synthesized Database Interactions”. ACM Transactions on Software Engineering and Methodology (TOSEM), 23(2):12:1-27, March 2014.

2013

  • Y. Wang, X. Wu, and X. Shi. “Using Aggregate Human Genome Data for Individual Identification”. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shanghai, China, Dec 18-21, 2013, pp. 410-415. (Regular paper, acceptance ratio 60/306 Best Paper Award )
  • L. Wu, X. Wu, A. Lu, and Z-H. Zhou. “A Spectral Approach to Detecting Subtle Anomalies in Graphs”.Journal of Intelligent Information Systems (JIIS), 41(2):313-337, 2013.
  • Y. Wang, X. Wu, J. Zhu, and Y. Xiang. “On Learning Cluster Coefficient of Private Networks”. Journal of Social Network Analysis and Mining (SNAM), 3(4):925-938, 2013.
  • Y. Wang and X. Wu. “Preserving Differential Privacy in Degree-correlation based Graph Generation”. Transactions on Data Privacy, 6(2):127-145, 2013.
  • Y. Wang, X. Wu, and L. Wu. “Differential Privacy Preserving Spectral Graph Analysis”. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD13), Gold Coast, Australia, April 14-17, 2013 (Regular paper with long presentation, acceptance ratio 39/363).
  • X. Ying, X. Wu, and Y. Wang. “On Linear Refinement of Differential Privacy-Preserving Query Answering”. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD13), Gold Coast, Australia, April 14-17, 2013 (Regular paper with long presentation, acceptance ratio 39/363, Best Application Paper Award).
  • K. Pan, X. Wu, and T. Xie. “Automatic Test Generation for Mutation Testing on Database Applications”.Proceedings of the 8th International Workshop on Auomation of Software Test (AST13), In conjunction with ICSE, San Francisco, CA, USA, May 18-19, 2013. (acceptance ratio 18/38)
  • X. Hu, A. Lu, and X. Wu. “Spectrum-based Network Visualization for Topology Analysis”. IEEE Computer Graphics and Applications, 33(1):58-68, 2013.

2012

  • Z. Luo, Y. Wang, and X. Wu. “Predicting Retweeting Behavior Based on Autoregressive Moving Average Model”.Proceedings of the 13th International Conference on Web Information System Engineering (WISE12), Paphos, Cyprus, Nov 28-30, 2012 (Runner-up award on WISE 2012 Challenge – Mining Track).
  • Y. Wang, X. Wu, J. Zhu, and Y. Xiang. “On Learning Cluster Coefficient of Private Networks”.Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM12), Istanbul, Turkey, August 26-29, 2012, pp.395-402 (Full paper, acceptance ratio 16%).
  • Z. Luo, X. Wu, W. Cai, and D. Peng. “Examining Multi-factor Interactions in Microblogging based on Log-linear Modeling”.Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM12), Istanbul, Turkey, August 26-29, 2012, pp. 189-193 (Short paper, acceptance ratio 16+25%).
  • L. Wu, X. Ying, X. Wu, A. Lu, and Z-H. Zhou. “Examining Spectral Space of Complex Networks with Positive and Negative Links”. International Journal of Social Network Mining (IJSNM), 1(1):91-111, 2012.

2011

  • K. Pan, X. Wu, and T. Xie. “Generating Program Inputs for Database Application Testing”. Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering (ASE),  Lawrence, Kansas, Nov 2011, pp. 73-82(full paper, acceptance ratio 37/252).
  • L. Wu, X. Ying, X. Wu and Z-H. Zhou. “Line Orthogonality in Adjacency Eigenspace with Application to Community Partition”. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI11), Barcelona, Spain, July 16-22, 2011, pp. 2349-2354(oral presentation, acceptance ratio 227/1325).
  • K. Pan, X. Wu, and T. Xie. “Database State Generation via Dynamic Symbolic Execution for Coverage Criteria”. Proceedings of the 4th International Workshop on Testing Database Systems(DBTest11), Athens, Greece, June 13, 2011.
  • L. Wu, X. Ying, X. Wu, A. Lu, and Z-H. Zhou. “Spectral Analysis of k-balanced Signed Graphs”. Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD11), Shenzhen, China, May 24-27, 2011, pp.1-12 (long presentation, acceptance ratio 32/331).
  • X. Wu and X. Ying. “A Tutorial of Privacy-Preservation of Graphs and Social Networks”, tutorial at the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD11), Shenzhen, China, May, 2011.
  • X. Ying, X. Wu, and D. Barbara. “Spectrum Based Fraud Detection in Social Networks”. Proceedings of the 27th IEEE International Conference on Data Engineering (ICDE11), Hannover, Germany, April 11-April 16, 2011, pp.912-923 (acceptance ratio 98/494).
  • X. Ying, L. Wu and X. Wu. “A Spectrum-based Framework for Quantifying Randomness of Social Networks”. IEEE Transactions on Knowledge and Data Engineering (TKDE), 23(12):1842-1856, 2011.
  • X. Ying and X. Wu. “On Link Privacy in Randomizing Social Networks”. Journal of Knowledge and Information System, 28(3):645-663, 2011.
  • L. Guo, X. Ying and X. Wu.   “Limiting Attribute Disclosure in Randomization based Microdata Release”. Journal of Computing Science and Engineering (JCSE), 5(3):169-182, 2011.

2010

  • L. Wu, X. Ying and X. Wu. “Reconstruction from Randomized Graph via Low Rank Approximation”. Proceedings of the 10th SIAM Conference on Data Mining (SDM10), Columbus, Ohio, April 29-May 1, 2010, pp.60-71. (acceptance ratio 82/351).
  • L. Guo, X. Ying and X. Wu.   “On Attribute Disclosure in Randomization based Privacy Preserving Data Publishing”. IEEE International Workshop on Privacy Aspects of Data Mining (PADM10), In conjunction with ICDM10, Sydney, Australia, Dec 14, 2010. (Invited for a journal publication)
  • L. Harrison, X. Hu, X. Ying, A. Lu, W. Wang and X. Wu. “Interactive Detection of Network Anomalies via Coordinated Multiple Views “. Proceedings of the 7th International Symposium on Visualization for Cyber Security (VizSec10), In conjunction with RAID, Ottawa, Canada, Sept 14, 2010.
  • X. Wu, X. Ying and L. Wu. “Analyzing Socio-technical Networks: a Spectrum Perspective”. Invited book chapter. Socio-technical Networks: Science and Engineering Design. Editors Fei Hu, Ali Mostashari and Jiang Xie. Taylor & Francis LLC, CRC Press, 2010.
  • H. Loftis, A. Waters, X. Wu and B. Chu. “Discovering Information Security Discussions among Debian Developers”. In the 19th USENIX Security Symposium, Washington DC, August 11-13, 2010 (poster).
  • X. Ying, X. Wu, and D. Barbara. “Spectrum Based Fraud Detection in Social Networks”. Proceedings of the 17th ACM International Conference on Computer and Communications Security (CCS10), Chicago, Oct 4-8, 2010, pp 747-749. (poster).

2009

  • L. Guo and X. Wu. “Privacy Preserving Categorical Data Analysis with Unknown Distortion Parameters”.  Transaction on Data Privacy, 2(3):185-205, 2009.
  • X. Wu, X.Ying, K. Liu and L. Chen. “A Survey of Algorithms for Privacy-Preservation of Graphs and Social Networks”. Invited book chapter. Managing and Mining Graph Data. Editors Charu C. Aggarwal and Haixun Wang. Kluwer Academic Publishers. August 2009.
  • X. Ying,X. Wu, K.Pan, and L. Guo. “On the Quantification of Identity and Link Disclosures in Randomizing Social Networks”. Invited book chapter. Advances in Information & Intelligent Systems. Editors Z.W. Ras and W. Ribarsky, Studies in Computational Intelligence, Vol 251, Springer, 2009.
  • X. Ying, K. Pan,X. Wu and L. Guo. “Comparisons of Randomization and K-degree Anonymization Schemes for Privacy Preserving Social Network Publishing “, Proceedings of the 3rd SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD), Paris, France, June 28, 2009.
  • X. Ying and X. Wu. “Graph Generation with Prescribed Feature Constraints”. Proceedings of the 9th SIAM Conference on Data Mining (SDM09), Sparks, Nevada, April 30-May 2, 2009, pp.966-977. (Oral presentation, acceptance ratio 55/351)
  • X. Ying and X. Wu. “On Randomness Measures for Social Networks”.  Proceedings of the 9th SIAM Conference on Data Mining (SDM09), Sparks, Nevada, April 30-May 2, 2009, pp.709-720. (Poster presentation, acceptance ratio 55+50/351)
  • X. Ying and X. Wu. “On Link Privacy in Randomizing Social Networks”. Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD09), Bangkok, Thailand, April 27-30, 2009, pp.28-39. (Regular paper, acceptance ratio: 39/338, Best Student Paper Runner-up Award) (PAKDD 2019 Most Influential Paper Award)

2008

  • X. Ying and X. Wu. “Randomizing Social Networks: a Spectrum Preserving Approach”. Proceedings of the 8th SIAM Conference on Data Mining (SDM08), Atlanta, Georgia, April 24-26, 2008, pp. 739-750. (full paper, acceptance ratio: 40/282)
  • L. Guo, S. Guo and X. Wu. “On Addressing Accuracy Concerns in Privacy Preserving Association Rule Mining”. Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD08), Osaka, Japan, May 20-23, 2008. (long paper, acceptance ratio: 37/312)
  • S.Guo, X.Wu and Y. Li. “Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining”, Journal of Knowledge and Information System 17(2):217-240, 2008. (online version)
  • L. Qiu, Y. Li and X. Wu. “Protecting Business Intelligence and Customer Privacy while Outsourcing Data Mining Tasks”.  Journal of Knowledge and Information System, 17(1):99-120, 2008. (online version)

2007

  • L.Guo, S. Guo and X.Wu. “Privacy Preserving Market Basket Data Analysis”. Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD07), Warsaw, Poland, Sept 17-21, 2007, pp.103-114. (acceptance ratio: 54/592)
  • S.Guo and X.Wu. “Deriving Private Information from Arbitrarily Projected Data”. Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD07), Nanjing, China, May 22-25, 2007, pp.84-95.  (acceptance ratio: 34/730)
  • Y .Li, L. Qiu and X. Wu. “Privacy Preserving Association Rule Mining with Bloom Filters”. Journal of Intelligent Information System, 29(3):253-278, 2007.  (online version)
  • X. Wu, Y. Wang, S. Guo and Y. Zheng. “Privacy Preserving Database Generation for Database Application Testing”, Fundamenta Informaticae, 78(4):595-612, 2007.

2006

  • S.Guo, X.Wu and Y. Li. “On the Lower Bound of Reconstruction Error for Spectral Filtering based Privacy Preserving Data Mining”. Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD06), Berlin, Germany, Sept 18-22, 2006, pp.520-527 (acceptance ratio: 122/685) 
  • L.Qiu, Y.Li and X.Wu.”An Approach to Outsourcing Data Mining Tasks while Protecting Business Intelligence and Customer Privacy”. Proceedings of the 2nd IEEE International Workshop on Privacy Aspects of Data Mining (PADM06), In conjunction with ICDM06, Hong Kong, Dec 2006. (acceptance ratio: 11/29)
  • H. Lu, Y. Li and X. Wu. “Disclosure Risk for Dynamic Two-Dimensional Contingency Tables”. Proceedings of the 2nd International Conference on Information Systems Security (ICISS06), Kolkata, India, Dec 17-21, 2006 ( acceptance ratio 24/79)
  • H. Lu, Y. Li and X. Wu. “Disclosure Analysis for Two-Way Contingency Tables”. Proceedings of the 2006 Privacy in Statistical Databases (PSD06), Rome, Italy, Dec 13-15, 2006
  • X. Wu and Y. Ye. “Exploring Gene Causal Interactions Using an Enhanced Constraint-based Method”. Pattern Recognition 39:2439-2449, 2006.
  • S. Guo, X. Wu and Y. Li. “Deriving Private Information from Perturbed Data Using IQR based Approach”, Second International Workshop on Privacy Data Management (PDM06), In conjunction with 22nd ICDE conference, Atlanta, April 2006.   
  • S. Guo and X. Wu. “On the Use of Spectral Filtering for Privacy Preserving Data Mining”,  Proceedings of the 21st ACM Symposium on Applied Computing (SAC06),  Dijon, France, April 23-27, 2006, pp. 622-626. (data mining track, acceptance ratio: 20/59)
  • X. Wu, S. Guo, and Y. Li. “Towards Value Disclosure Analysis in Modeling General Databases”, Proceedings of the 21st ACM Symposium on Applied Computing (SAC06), Dijon, France, April 23-27, 2006, pp.617-621. (data mining track, acceptance ratio: 20/59)
  • X. Wu. “Incorporating Large Unlabeled Data to Enhance EM Classification”, Journal of Intelligent Information System,  26(3): 211-226,  May 2006.

2005

  • Y. Wang and X. Wu. “Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation”. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM05), Houston,  pp.482-489, Nov 27-30, 2005.  (acceptance ratio: 69/630)
  • X. Wu, C. Sanghvi, Y. Wang and Y. Zheng. “Privacy Aware Data Generation for Testing Database Applications”. Proceedings of the 9th International Database Engineering and Application Symposium (IDEAS05), Montreal, Canada, July 25-27, 2005. pp. 317-326. (acceptance ratio: 30/144).
  • Y. Ye and X. Wu. “Efficient Causal Interaction Learning with Applications in Microarray”, Proceedings of 15th International Symposium on Methodologies for Intelligent Systems (ISMIS05), Saratoga Spring, New York, May 25-28, 2005, pp. 622-630.
  • X. Wu, Y. Wang and Y. Zheng. “Statistical Database Modeling for Privacy Preserving Database Generation”, Proceedings of 15th International Symposium on Methodologies for Intelligent Systems (ISMIS05), Saratoga Spring, New York, May 25-28, 2005, pp. 382-390.
  • X. Wu, Y. Wu, Y. Wang and Y. Li. “Privacy Aware Market Basket Data Set Generation: A Feasible Approach for Inverse Frequent Set Mining”, Proceedings of the 5th SIAM International Conference on Data Mining(SDM05),Newport Beach, CA, April 21-23, 2005, pp.103-114.  (acceptance ratio:40/218).

 

2004

  • Y. Wang, X. Wu and Y. Zheng. “Privacy Preserving Data Generation for Database Application Performance Testing”. Proceedings of 1st International Conference on Trust and Privacy in Digital Business (TrustBus04), Zaragoza, Spain, Sept 2004.
  • Y. Ye, X. Wu, K. Subramanian and L. Zhang. “GenExplore: Interactive Exploration of Gene Interactions from Microarray Data”, ICDE 2004 (demo paper, acceptance ratio 16/28).

2003

  • X. Wu, Y. Ye and L. Zhang. “Graphical Modeling Based Gene Interaction Analysis for Microarray Data”, SIGKDD Explorations,5(2):91-100, Dec 2003.
  • D. Barbara, X. Wu. “Approximate Median Polish Algorithm for Large Multidimensional Data Sets”, Journal of Knowledge and Information System,5(4):416-438, Oct 2003.
  • X. Wu, Y. Wang and Y. Zheng. “Privacy Preserving Database Application Testing”, Workshop on Privacy in the Electronic Society, In conjunction with 10th ACM CCS, Washington D.C., Oct 2003. pp.118-128. (acceptance ratio: 16/50).  
  • X. Wu, D. Barbara, Y. Ye. “Screening and Interpreting Multi-item Associations Based on Log-linear Modeling”, Proceedings of ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining (SIGKDD2003), Washington DC, August 24-27, 2003, pp.276-285.(acceptance ratio: 34/258).
  • X. Wu, Y. Ye, K. Subramanian and  L. Zhang. “Interactive Gene Interaction Analysis Using Graphical Gaussian Model”, Proceedings of BIOKDD03,  In conjunction with KDD 2003, Washington DC, August 2003, pp.63-69. (acceptance ratio: 9/24).
  • X. Wu, D. Barbara, L. Zhang and Y. Ye. “Gene Interaction Analysis using All k-way Interaction Loglinear: A Case Study on Yeast Data”, Proceedings of ICML 2003 Workshop: Machine Learning in Bioinformatics, Washington DC, August 2003, pp.38-45.
  • X. Wu, D. Barbara. “Using Fractals to Compress Real Data Sets: Is It Feasible? “, Proceedings of Second Workshop on Fractals, Power Laws and Other Next Generation Data Mining Tools.  In conjunction with KDD 2003, Washington DC, August 2003, pp.6-11.
  • X. Wu, D. Barbara. “Compressing High Dimensional Datasets by Fractals”, Proceedings of IEEE Data Compression Conference(DCC03),Snowbird, Utah, March 2003, pp.452(accepted as a poster, full paper available on request)

2002

  • X. Wu, J. Fan, K. Subramanian,  “B-EM: A Classifier Incorporating Bootstrap with EM Approach for Data Mining”, Proceedings of ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining (SIGKDD02), Edmonton, Canada, July 2002, pp.670-675. (acceptance ratio: 88/308).
  • X. Wu, D. Barbara. “Modeling and Imputation of Large Incomplete Multidimensional Datasets” , Proceedings of the fourth Int’l Conference on Data Warehousing and Knowledge Discovery (DaWak02), Aix-en-Provence, France, Sept 2002, pp.286-295. (acceptance ratio: 32/100+)
  • X. Wu, D. Barbara. “Learning Missing Values from Summary Constraints”, SIGKDD Explorations, 4(1):21-30, June 2002.

2001 or before

  • D. Barbara, X. Wu. “Loglinear-Based Quasi Cubes.” Journal of Intelligent Information Systems, 16(3):255-276, August 2001.
  • X. Wu. “Approximate Algorithms for Data Warehousing and Data Mining.” Ph.D. dissertation. ISBN, 0-493-23023-8, August, 2001.
  • D. Barbara, X. Wu. “Finding Dense Clusters in Hyperspace: An Approach Based on Row Shuffling.” Proceedings of the 2nd Int’l Conference on Web Age Information Systems(WAIM01), Xian, China, July 2001, pp.305-316.
  • D. Barbara, X. Wu. “Supporting Online Queries in ROLAP.” Proceedings of the 2nd Int’l Conference on Data Warehousing and Knowledge Discovery (DaWak00), London, September 2000, pp.234-243.
  • D. Barbara, X. Wu. “Using Loglinear Models to Compress Datacubes.” Proceedings of the 1st Int’l Conference on Web-Age Information Management(WAIM00), Shanghai, China, June 2000, pp.311-322.
  • D. Barbara, X. Wu. “The Role of Approximations in Maintaining and Using Aggregate Views.” IEEE Bulletin on Database Engineering , Vol. 22, Num. 4, Dec 1999, pp. 15-21.
  • D. Barbara, X. Wu. “Using Approximations to Scale Exploratory Data Analysis in Datacubes.” Proceedings of the 1999 ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining (SIGKDD99), San Diego, August 1999, pp.382-386.(acceptance ratio: 52/280).