Guansong Pang

Guansong Pang

Australian Institute for Machine Learning - Projects

Faculty of Engineering, Computer and Mathematical Sciences


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.

  • Journals

    Year Citation
    2021 Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., . . . Xia, Y. (2021). Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Transactions on Medical Imaging, 40(3), 879-890.
    DOI Scopus3 WoS1 Europe PMC10
    2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features.. CoRR, abs/2101.10030.
    2021 Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images.. CoRR, abs/2103.03423.
    2021 Yan, C., Pang, G., Bai, X., Liu, C., Xin, N., Gu, L., & Zhou, J. (2021). Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss. IEEE Transactions on Multimedia, 1.
    DOI
    2021 Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2), 38 pages.
    DOI Scopus3 WoS1
    2021 Pang, G., Cao, L., & Chen, L. (2021). Homophily outlier detection in non-IID categorical data. Data Mining and Knowledge Discovery.
    DOI
    2020 Pang, G., & Cao, L. (2020). Heterogeneous univariate outlier ensembles in multidimensional data. ACM Transactions on Knowledge Discovery from Data, 14(6), 68-1-68-27.
    DOI Scopus1
    2020 Zheng, D., Pang, G., Liu, B., Chen, L., & Yang, J. (2020). Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors.. Bioinformatics, 36(12), 3693-3702.
    DOI Scopus1 WoS1
    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(5), 853-866.
    DOI Scopus10
    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.
    DOI Scopus10
    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
    Pang, G., Pham, N. T. A., Baker, E., Bentley, R., & Hengel, A. V. D. (n.d.). Deep Multi-task Learning for Depression Detection and Prediction in
    Longitudinal Data.
  • Conference Papers

    Year Citation
    2021 Pang, G., Cao, L., & Aggarwal, C. (2021). Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, WDSM'21 (pp. 1127-1130). online: ACM.
    DOI
    2020 Zhao, J., Yang, Y., Pang, G., Lv, L., Shang, H., Sun, Z., & Yang, W. (2020). Learning Discriminative Neural Sentiment Units for Semi-supervised Target-Level Sentiment Classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12085 LNAI (pp. 798-810). Switzerland: Springer.
    DOI Scopus1
    2020 Wang, H., Pang, G., Shen, C., & Ma, C. (2020). Unsupervised representation learning by predicting random distances. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2021-January (pp. 2950-2956). online: AAAI Press.
    Scopus5
    2020 Pang, G., Yan, C., Shen, C., van den Hengel, A., & Bai, X. (2020). Self-trained deep ordinal regression for end-to-end video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (pp. 12170-12179). online: IEEE.
    DOI Scopus5
    2019 Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. In KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 353-362). New York: Association of Computing Machinery.
    DOI Scopus25 WoS10
    2019 Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., & Hancock, E. (2019). Deep hashing by discriminating hard examples. In MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia (pp. 1535-1542). online: ACM.
    DOI Scopus4 WoS2
    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.
    Scopus21
    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 Scopus29
    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 Vol. Part F131841 (pp. 807-816). online: ACM.
    DOI Scopus14
    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 Vol. 0 (pp. 1937-1943). online: IJCAI.
    DOI Scopus13
    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 Vol. 0 (pp. 2585-2591). online: IJCAI.
    DOI Scopus21
    2017 Pang, G., Cao, L., Chen, L., & Liu, H. (2017). Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In Proceedings - IEEE International Conference on Data Mining, ICDM Vol. 0 (pp. 410-419).
    DOI Scopus20
    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 Vol. 2016-January (pp. 1902-1908). Online: AAAI Press / International Joint Conferences on Artificial Intelligence.
    Scopus36
    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 Scopus17
    2013 Pang, G., Jiang, S., & Chen, D. (2013). A Simple Integration of Social Relationship and Text Data for Identifying Potential Customers in Microblogging. In Unknown Conference (pp. 397-409). Springer Berlin Heidelberg.
    DOI
    2013 Pang, G., Jin, H., & Jiang, S. (2013). An effective class-centroid-based dimension reduction method for text classification. In Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion. ACM Press.
    DOI
  • Current Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2021 Co-Supervisor Computer Vision Doctor of Philosophy Doctorate Full Time Mr Yongtao Ge
    2021 Principal Supervisor Joint Grouping and Labeling is Solved by Using Graphical Models Doctor of Philosophy Doctorate Full Time Miss Jinchao Ge
    2020 Co-Supervisor Weakly Supervised Semantic Segmentation Doctor of Philosophy Doctorate Full Time Mr Choubo Ding
    2020 Co-Supervisor Text Visual Question Answering Doctor of Philosophy Doctorate Full Time Mr Xinyu Wang
    2019 Co-Supervisor Semantic 3D Scene Reconstruction Doctor of Philosophy Doctorate Full Time Mr Libo Sun
  • Past Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2020 - 2021 Co-Supervisor Connecting Machine Learning to Causal Structure Learning with Jacobian Matrix Master of Philosophy Master Full Time Mr Xiongren Chen
    2019 - 2021 Co-Supervisor Efficient Fully-Convolutional Networks for Image Perception Doctor of Philosophy Doctorate Full Time Mr Hao Chen
  • Email: guansong.pang@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor G
  • Room: G.29.B
  • Org Unit: The University of Adelaide

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