Guansong Pang

Guansong Pang

Australian Institute for Machine Learning - Projects

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


I am a research fellow with the Australian Institute for Machine Learning at The University of Adelaide. My current research interests include anomaly detection, deep learning, weakly supervised learning, reinforcement learning, non-IID learning, fake news detection, and their applications in cybersecurity, Fintech, and healthcare.

My research interests are generally on machine learning and their applications, with a recent focus on deep learning, anomaly detection, reinforcement learning, person reidentification, and fake news detection.

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  • Journals

    Year Citation
    2019 Jian, S., Pang, G., Cao, L., Lu, K., & Gao, H. (2019). CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning. IEEE Trans. Knowl. Data Eng., 31, 853-866.
    DOI
    2018 Pang, G., Cao, L., Chen, L., & Liu, H. (2018). Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. CoRR, abs/1806.04808.
    2016 Pang, G., Ting, K. M., Albrecht, D. W., & Jin, H. (2016). ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets. The Journal of Artificial Intelligence Research, 57, 593-620.
    2015 Pang, G., Jin, H., & Jiang, S. (2015). CenKNN: a scalable and effective text classifier. Data Mining and Knowledge Discovery, 29(3), 593-625.
    DOI
    2013 Pang, G., & Jiang, S. (2013). A generalized cluster centroid based classifier for text categorization. Information Processing & Management, 49(2), 576-586.
    DOI
    2012 Jiang, S., Pang, G., Wu, M., & Kuang, L. (2012). An improved K-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503-1509.
    DOI
  • Conference Papers

    Year Citation
    2019 Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 353-362).
    DOI
    2018 Pang, G., Cao, L., Chen, L., Lian, D., & Liu, H. (2018). Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 (pp. 3892-3899). online: AAAI.
    2018 Pang, G., Cao, L., Chen, L., & Liu, H. (2018). Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2041-2050). New York: Association for Computing Machinery.
    DOI
    2017 Pang, G., Xu, H., Cao, L., & Zhao, W. (2017). Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06 - 10, 2017 (pp. 807-816). online: ACM.
    DOI
    2017 Jian, S., Cao, L., Pang, G., Lu, K., & Gao, H. (2017). Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017 (pp. 1937-1943). online: IJCAI.
    DOI
    2017 Pang, G., Cao, L., Chen, L., & Liu, H. (2017). Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017 (pp. 2585-2591). online: IJCAI.
    DOI
    2016 Pang, G., Cao, L., Chen, L., & Liu, H. (2016). Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings. In IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain (pp. 410-419). Online: IEEE.
    DOI
    2016 Pang, G., Cao, L., & Chen, L. (2016). Outlier Detection in Complex Categorical Data by Modeling the Feature Value Couplings. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016 (pp. 1902-1908). Online: AAAI Press / International Joint Conferences on Artificial Intelligence.
    2015 Pang, G., Ting, K. M., & Albrecht, D. W. (2015). LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. In Proceedings of the IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 623-630). Online: IEEE.
    DOI
    2013 Pang, G., Jiang, S., & Chen, D. (2013). A Simple Integration of Social Relationship and Text Data for Identifying Potential Customers in Microblogging. In Advanced Data Mining and Applications, 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I (pp. 397-409).
    DOI
    2013 Pang, G., Jin, H., & Jiang, S. (2013). An effective class-centroid-based dimension reduction method for text classification. In 22nd International World Wide Web Conference, WWW ’13, Rio de Janeiro, Brazil, May 13-17, 2013, Companion Volume (pp. 223-224).
    DOI

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