See Google Scholar or DBLP for more detail.

*  Co-first author; ** Corresponding author

Preprints

1. Guoguo Ai, Guansong Pang, Hezhe Qiao, Yuan Gao, and Hui Yan. "GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers." arXiv preprint arXiv:2411.17296 (2024). [pdf] [code]

2. Jiawen Zhu, Yew-Soon Ong, Chunhua Shen, and Guansong Pang**. "Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection." arXiv preprint arXiv:2410.10289 (2024). [pdf] [code]

3. Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, and Guansong Pang**. "Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts." arXiv preprint arXiv:2410.14886 (2024). [pdf] [code]

4. Sinong Zhao, Wenrui Wang, Hongzuo Xu, Zhaoyang Yu, Qingsong Wen, Gang Wang, and Guansong Pang**. "Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling." arXiv preprint arXiv:2410.12206 (2024). [pdf] [code]

5. Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, and Guansong Pang**. "Deep Graph Anomaly Detection: A Survey and New Perspectives." arXiv preprint arXiv:2409.09957 (2024). [pdf] [project]

6. Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang**, Peng Wang, and Yanning Zhang. "Deep Learning for Video Anomaly Detection: A Review." arXiv preprint arXiv:2409.05383 (2024). [pdf]

7. Wenjun Miao, Guansong Pang**, Trong-Tung Nguyen, Ruohang Fang, Jin Zheng, and Xiao Bai. "OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning." arXiv preprint arXiv:2407.06045 (2024). [pdf] [code]

8. Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Zhucun Xue, Yong Liu, Guansong Pang, and Dacheng Tao. "Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark." arXiv preprint arXiv:2404.10760 (2024). [pdf]

9. Yukun Li, Guansong Pang**, Wei Suo, Chenchen Jing, Yuling Xi, Lingqiao Liu, Hao Chen, Guoqiang Liang, Peng Wang. "CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning."arXiv preprint arXiv:2403.10245(2024). [pdf]

10. Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, and Weiming Shen. "A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect." arXiv preprint arXiv:2401.16402 (2024). [pdf]

11. Qizhou Wang, Guansong Pang**, Mahsa Salehi, Wray Buntine, and Christopher Leckie. "Open-Set Graph Anomaly Detection via Normal Structure Regularisation." arXiv preprint arXiv:2311.06835 (2023). [pdf]

12. Ruohuan Fang, Guansong Pang**, Lei Zhou, Xiao Bai, and Jin Zheng. "Unsupervised Recognition of Unknown Objects for Open-World Object Detection." arXiv preprint arXiv:2308.16527 (2023). [pdf] [code]

Tutorials

1. Guansong Pang, Joey Tianyi Zhou, Radu Tudor Ionescu, Yu Tian, and Kihyuk Sohn. "Recent Advances in Anomaly Detection". In: CVPR'23. Vancouver, Canada. [website] [slides] [video]

2. Ye Zhu, Guansong Pang, Sutharshan Rajasegarar, Xuyun Zhang, and Gang Li. "Moving Beyond Traditional Anomaly Detection." In: IJCAI'23. Macao, China. [webiste]

3. Guansong Pang and Charu Aggarwal. "Toward Explainable Deep Anomaly Detection". In: KDD'21, pp. 4056-4057, 2021. [pdf] [slides] [website]

4. Guansong Pang, Longbing Cao,  and Charu Aggarwal. "Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities". In: WSDM'21, pp. 1127–1130, 2021. Jerusalem, Israel. [pdf] [slides] [video] [website]

5. Longbing Cao, Philip Yu, and Guansong Pang. "Behavior Analytics: Methods and Applications". In: KDD'18. London, United Kingdom. [slides] [website]

6. Longbing Cao, Philip Yu, Guansong Pang, and Chengzhang Zhu. "Non-IID Learning". In: KDD'17. Halifax, Canada. [slides] [website]

Editorials

• 1. Guansong Pang. "Artificial Intelligence for Natural Disaster Management." IEEE Intelligent Systems 37.6 (2022): 3-6.

  2. Guansong Pang, Charu Aggarwal, Chunhua Shen, and Nicu Sebe. "Editorial: Deep Learning for Anomaly Detection".  IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 33.6 (2022):  2282 - 2286. [pdf]

  3. Guansong Pang. "The AI Chip Race". IEEE Intelligent Systems 37.2 (2022): 111-112. [pdf]

  4. Guansong Pang. "AI in Beijing 2022 Olympic Winter Games". IEEE Intelligent Systems 37.1 (2022): 110-110. [pdf]

  5. Guansong Pang, Fabrizio Angiulli, Mihai Cucuringu, and Huan Liu. "Guest Editorial: Non-IID Outlier Detection in Complex Contexts". IEEE Intelligent Systems 36.3 (2021): 3-4. [pdf]

Refereed Publications

2025

• 1. Anindya Das, Guansong Pang, and Monowar Bhuyan. "Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination." In: WACV'25, to appear.  [pdf] [code]

2024

• 2. Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, and Guansong Pang**. "Generative Semi-supervised Graph Anomaly Detection." In: NeurIPS'24, to appear. Acceptance rate: 25.8% (4043/15671 ).  [pdf] [code]

  3. Chaoxi Niu, Guansong Pang**, Ling Chen, and Bing Liu. "Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach." In: NeurIPS'24, to appear. Acceptance rate: 25.8% (4043/15671 ).  [pdf] [code]

  4. Wenjun Miao, Guansong Pang**, Jin Zheng, and Xiao Bai. "Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation." In: NeurIPS'24, to appear. Acceptance rate: 25.8% (4043/15671 ).  [pdf] [code]

  5. Choubo Ding and Guansong Pang**. "Improving Out-of-distribution Detection with Disentangled Foreground and Background Features". In: ACM Multimedia'24, to appear.  Acceptance rate: 26.2% (1149/4385). [pdf] [code]

  6. Peng Wu, Xuerong Zhou, Guansong Pang**, Zhiwei Yang, Qingsen Yan, Peng Wang, and Yanning Zhang. "Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts." In: ACM Multimedia'24, to appear. Acceptance rate: 26.2% (1149/4385).  [pdf] [code]

  7. Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, and Hongzhi Yin. "Unraveling the 'Anomaly' in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution." In: ICDE'24, to appear. [pdf] [code]

  8. Jiawen Zhu, Choubo Ding, Yu Tian, and Guansong Pang**. "Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection." In: CVPR'24, to appear. Acceptance rate: 23.6% (2720/11532) [pdf] [code]

  9. Jiawen Zhu and Guansong Pang**. "Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Normal Sample Prompts." In: CVPR'24, to appear. Acceptance rate: 23.6% (2720/11532) [pdf] [code]

  10. Peng Wu, Xuerong Zhou, Guansong Pang**, Yujia Sun, Jing Liu, Peng Wang, and Yanning Zhang. "Open-Vocabulary Video Anomaly Detection." In: CVPR'24, to appear. Acceptance rate: 23.6% (2720/11532) [pdf]

  11. Tianqi Li, Guansong Pang**, Wenjun Miao, Xiao Bai, and Jin Zheng. "Learning Transferable Negative Prompts for Out-of-Distribution Detection." In: CVPR'24, to appear. Acceptance rate: 23.6% (2720/11532). [pdf] [code]

  12. Qihang Zhou*, Guansong Pang*, Yu Tian, Shibo He, and Jiming Chen. "AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection." In: ICLR'24, to appear. Acceptance rate:  31% (~2251/7262). [pdf] [code]

  13. Ruohuan Fang, Guansong Pang**, and Xiao Bai. "Simple Image-level Classification Improves Open-vocabulary Object Detection." In: AAAI'24, to appear. Acceptance rate:  23.75% (2342/9862). [pdf] [code]

  14. Wenjun Miao, Guansong Pang**, Xiao Bai, Tianqi Li, and Jin Zheng. "Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning." In: AAAI'24, to appear. Acceptance rate:  23.75% (2342/9862). [pdf] [code]

  15. Peng Wu, Xuerong Zhou, Guansong Pang**, Lingru Zhou, Qingsen Yan, Peng Wang, and Yanning Zhang. "VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection." In: AAAI'24, to appear. Acceptance rate:  23.75% (2342/9862). [pdf] [code]

  16. Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, and Qingsong Wen. "LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Anomaly Detection." In: WWW'24, to appear. Acceptance rate: 20.2% (~405/2008). [pdf]

  17. Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, and Shuiguang Deng. "Cluster-Wide Task Slowdown Detection in Cloud Systems." In: KDD'24, to appear. Acceptance rate: 20.0% (~409/2046). [pdf]

  18. Chaoxi Niu, Guansong Pang**, and Ling Chen. "Graph Continual Learning with Debiased Lossless Memory Replay." In: ECAI'24, to appear. Acceptance rate: 23% (547/2344). [pdf] [code]

  19. Yutong Chen, Hongzuo Xu, Guansong Pang**, Hezhe Qiao, Yuan Zhou, and Mingsheng Shang. "Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection." In: ECMLPKDD'24, to appear. Acceptance rate: 24% (~198/826). [pdf] [code]

  20. Rongrong Ma, Guansong Pang**, Ling Chen. "Imbalanced Graph Classification with Multi-Scale Oversampling Graph Neural Networks." In: IJCNN'24, to appear [pdf] [code]

  21. Choubo Ding, Guansong Pang. "Zero-Shot Out-Of-Distribution Detection with Outlier Label Exposure." In: IJCNN'24, to appear. [pdf] [code]

  22. Rongrong Ma, Guansong Pang**, and Ling Chen. "Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks." Neural Networks, 2024. To appear. [pdf] [code]

  23. Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, and Guansong Pang. "Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection." IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024. To appear. [pdf] [code]

  24. Tianqi Li, Guansong Pang**, Xiao Bai, Jin Zheng, Lei Zhou, and Xin Ning. "Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds." Pattern Recognition (PR), 2024. To appear. [pdf] [code]

  25. Chaoxi Niu, Guansong Pang**, and Ling Chen. "Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning." IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024. To appear. [pdf] [code]

  26. Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, et al. "Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects." IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. To appear. [pdf]

  27. Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang**, and Jian Yang. "A deep learning method to predict bacterial ADP-ribosyltransferase toxins." Bioinformatics, 2024. To appear. [pdf] [code]

  28. Jitendra Singh Malik, Hezhe Qiao, Guansong Pang**, and Anton van den Hengel. "Deep learning for hate speech detection: a comparative study." International Journal of Data Science and Analytics (JDSA), 2024. To appear. [pdf] [code]

2023

• 29. Hezhe Qiao and Guansong Pang**. "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection." In: NeurIPS'23, to appear. Acceptance rate:  26.1% (~3221/12343). [pdf] [code]

  30. Tri Cao, Jiawen Zhu, and Guansong Pang**. "Anomaly Detection under Distribution Shift." In: ICCV'23, to appear. Acceptance rate:  26.15% (2160/8260).  [pdf] [code]

  31. Cheng Yan, Zhang Shiyu, Yang Liu, Guansong Pang**, and Wenjun Wang. "Prediction Diffusion Model for Video Anomaly Detection". In: ICCV'23, to appear.  Acceptance rate:  26.15% (2160/8260).[pdf] [code]

  32. Guansong Pang, Chunhua Shen, Huidong Jin, and Anton van den Hengel. "Deep Weakly-supervised Anomaly Detection". In: KDD'23. Acceptance rate: 22.1% (313/1416).  [pdf] [code]

  33. Haoyu Wang*, Guansong Pang*, Peng Wang*, Lei Zhang, Wei Wei, and Yanning Zhang. "Glocal Energy-based Learning for Few-Shot Open-Set Recognition". In: CVPR'23. Acceptance rate: 25.8% (2360/9155). [pdf] [code]

  34. Qizhou Wang, Guansong Pang**, Mahsa Salehi, Wray Buntine, and Christopher Leckie. "Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment." In: AAAI'23. Acceptance rate: 19.6% (1721/8777). [pdf][code][data]

  35. Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation." In: ICCV'23, to appear. Acceptance rate:  26.15% (2160/8260). [pdf] [code]

  36. Chaoxi Niu, Guansong Pang**, and Ling Chen. "Graph-level Anomaly Detection via Hierarchical Memory Networks". In: ECML/PKDD'23. Acceptance rate:  24.0% (199/830). [pdf] [code]

  37. Zhong Zhuang, Kai Ming Ting, Guansong Pang, and Shuaibin Song. "Subgraph Centralization: A Necessary Step in Graph Anomaly Detection. " In: SDM'23. Acceptance rate: 27.4% (105/459). [pdf][code]

  38. Jiaxi Li, Guansong Pang, Ling Chen, and Mohammad-Reza Namazi-Rad. "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks." In: DSAA'23, to appear. Acceptance rate:  24%. [pdf] [code] Best Paper Award - Applications Track

  39. Hongzuo Xu, Guansong Pang**, Yijie Wang, and Yongjun Wang. "Deep Isolation Forest for Anomaly Detection." IEEE Transactions on Knowledge and Data Engineering (TKDE) 35(12): 12591-12604 (2023). [pdf] [code] [data]

  40. Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, and Yongjun Wang. "RoSAS: Deep Semi-supervised Anomaly Detection with Contamination-resilient Continuous Supervision." Information Processing & Management (IP&M) 60(5): 103459 (2023). [pdf] [code]

  41. Yu Tian*, Fengbei Liu*, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, and Gustavo Carneiro. "Self-supervised Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images." Medical Image Analysis (MedIA) 90: 102930 (2023). [pdf] [code]

  42. Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, and Lianhua Chi. "MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks". ACM Transactions on Knowledge Discovery from Data (TKDD)  17(4): 55:1-55:24 (2023). [pdf]

2022

• 43. Yu Tian, Yuyuan Liu, Guansong Pang**, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. " Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes." In: ECCV'22 (Oral), pp.246-263, 2022. Acceptance rate: 28% (1650/5803). Oral: 2.7% (158/5803). [pdf] [code]

  44. Choubo Ding*, Guansong Pang*, and Chunhua Shen. "Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection". In: CVPR'22, pp. 7388-7398, 2022. Acceptance rate: 25.33% (2067/8161). [pdf] [code] [data]

  45. Yuanhong Chen*, Yu Tian*, Guansong Pang, and Gustavo Carneiro. "Deep One-class Classification via Interpolated Gaussian Descriptor". In: AAAI'22, 36 (1), 383-392, 2022. Acceptance rate: 14.96% (1349/9020). Oral. [pdf] [code] [data]

  46. Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, and Anton Van Den Hengel. "Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification." In: PAKDD'22, pp. 236-248, 2022. Acceptance rate: 19.30% (121/627). [pdf]

  47. Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W. Verjans, and Gustavo Carneiro. "Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection." In: MICCAI'22, pp. 88-98, 2022. Acceptance rate: 31.3% (574/1831). [pdf] [code] 

  48. Rongrong Ma, Guansong Pang**, Ling Chen, and Anton van den Hengel. "Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation." In: WSDM'22, pp. 704-714, 2022. Acceptance rate: 20.23% (159 /786 ). [pdf] [code] [data]

  49. Cheng Yan*, Guansong Pang*, Xiao Bai, Jun Zhou, and Lin Gu.  “Beyond Triplet Loss:  Person Re-identification with Fine-grained Difference-aware Pairwise Loss.” IEEE Transactions on Multimedia (TMM), 24, pp. 1665-1677, 2022. [pdf] [preprint] [code] ESI Highly Cited Papers (top 1%) 

  50. Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel. "Deep learning for anomaly detection: A review". ACM Computing Survey (CSUR)  54, 2, Article 38 (January 2022), 38 pages. [pdf] [preprint] [tutorial] ESI Highly Cited Papers (top 1%) & ESI Hot Papers (top 0.1%)

  51. Xiaoyu Xu, Guansong Pang**, Di Wu, and Mingsheng Shang. "Joint Hyperbolic and Euclidean Geometry Contrastive Graph Neural Networks". Information Sciences (INS), 609 (2022) 799–815, 2022. [pdf] [code]

  52. Qing Li, Guansong Pang, and Mingsheng Shang. "An Efficient Annealing-Assisted Differential Evolution for Multi-parameter Adaptive Latent Factor Analysis". Journal of Big Data, 9:95, 2022. [pdf]

2021

• 53. Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao. "Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data." In: KDD'21 , pp. 1298-1308, 2021. Acceptance rate: 15.4% (238/1541). [pdf]

  54. Cheng Yan, Guansong Pang**, Jile Jiao, Xiao Bai, Xuetao Feng, and Chunhua Shen. "Occluded Person Re-Identification with Single-scale Global Representations." In: ICCV'21, pp. 11875-11884, 2021. Acceptance rate: 25.9% (1617/6236). Oral: 3.4% (210/6236). [pdf][code]

  55. Cheng Yan*, Guansong Pang*, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, and Jingjing Li. "BV-Person: A Large-scale Dataset for Bird-view Person Re-identification" In: ICCV'21, pp. 10943-10952, 2021. Acceptance rate: 25.9% (1617/6236). [pdf]

  56. Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, and Gustavo Carneiro. "Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning".  In: ICCV'21, pp. 4975-4986, 2021. Acceptance rate: 25.9% (1617/6236). [pdf][code][data]

  57. Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh, and Gustavo Carneiro. "Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images." In: MICCAI'21. Acceptance rate: 32.7% (533/1631)  [pdf] [code]

  58. Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia. "Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection". IEEE Transactions on Medical Imaging (TMI), 40.3, pp. 879 - 890, 2021. [pdf] [preprint] ESI Highly Cited Papers (top 1%)

  59. Guansong Pang, Longbing Cao, and Ling Chen. “Homophily outlier detection in non-IID categorical data". Data Mining and Knowledge Discovery (DMKD), 35(4): 1163-1224 (2021). [pdf] [preprint] [code]

2020

• 60. Hu Wang*, Guansong Pang*, Chunhua Shen, and Congbo Ma. "Unsupervised Representation Learning by Predicting Random Distances", In: IJCAI'20. Yokohama, Japan. Acceptance rate: 12.6% (592/4717). [pdf] [code] 

  61. Guansong Pang*, Cheng Yan*, Chunhua Shen, Xiao Bai, and Anton van den Hengel. "Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection", In: CVPR'20. Seattle, US. Acceptance rate: 22.1% (1470/6656). [pdf]

  62. Jingjing Zhao, Yao Yang, Guansong Pang, Lei Lv, Hong Shang, Zhongqian Sun, and Wei Yang. "Learning Discriminative Neural Sentiment Units for Semi-supervised Target-level Sentiment Classification", In: 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'20),pp. 798-810, 2020. Singapore. Acceptance rate: 21.5% (135/628). [pdf]

  63. Guansong Pang and Longbing Cao. "Heterogeneous Univariate Outlier Ensembles in Multidimensional Data". ACM Transactions on Knowledge Discovery from Data (TKDD), 14.6, Article 68, 27 pages, 2020. [pdf] [code]

  64. Dandan Zheng*, Guansong Pang*, Bo Liu, Lihong Chen and Jian Yang. "Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors". Bioinformatics, 36.12, pp. 3693–3702, 2020. [pdf] [code] [data]

2019 and before

• 65. Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, and Edwin Hancock. "Deep Hashing by Discriminating Hard Examples", In: 27th ACM International Conference on Multimedia (ACM Multimedia'19), pp. 1535-1542. Nice, France. Acceptance rate: 26.9% (252/936). [pdf]

  66. Guansong Pang, Chunhua Shen, and Anton van den Hengel. "Deep Anomaly Detection with Deviation Networks", In: 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19). Anchorage, US. Acceptance rate: 9.2% (110/1200) (Oral presentation). [pdf] [code] [video] [slides] [data]

  67. Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu, and Hang Gao. “ CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning". IEEE Transactions on Knowledge and Data Engineering (TKDE), 31.5, pp. 853-866, 2019. [code]

  68. Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. "Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection", In: 24th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18). London, UK. Acceptance rate: 10.9%(107/983) (Oral presentation) . [pdf] [code] [video] [data]

  69. Guansong Pang, Longbing Cao, Ling Chen, Defu Lian and Huan Liu. "Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data", In: 32nd AAAI Conference on Artificial Intelligence (AAAI'18). New Orleans, US. Acceptance rate: 24.6% (933/3800). [pdf] [code]

  70. Guansong Pang, Longbing Cao, Ling Chen and Huan Liu. "Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection". In: 26th International Joint Conference on Artificial Intelligence (IJCAI'17). Acceptance rate: 26.0% (660/2540). [pdf] [slides] [code] [data]

  71. Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu and Hang Gao. "Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning". In: 26th International Joint Conference on Artificial Intelligence (IJCAI'17). Acceptance rate: 26.0% (660/2540). [pdf] [code]

  72. Guansong Pang, Hongzuo Xu, Longbing Cao, Wentao Zhao. "Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data". In: 26th ACM International Conference on Information and Knowledge Management (CIKM'17). Singapore. Acceptance rate: 20.0% (171/855) (Long paper). [pdf] [slides] [code] [data]

  73. Guansong Pang, Longbing Cao, and Ling Chen. “Outlier Detection in Complex Categorical Data by Modelling Feature Value Couplings”. In: 25th International Joint Conference on Artificial Intelligence (IJCAI'16). AAAI Press, pp. 1902–1908, 2016. New York City, US. Acceptance rate: 24.0% (551/2294). [pdf] [slides] [code] [data]

  74. Guansong Pang, Longbing Cao, Ling Chen and Huan Liu. "Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings." In: 2016 IEEE International Conference on Data Mining (ICDM'16). Barcelona, Spain. Acceptance rate: 8.6% (78/904) (Full paper). [pdf] [code] [data]

  75. Guansong Pang, Kai Ming Ting, David Albrecht and Huidong Jin. “ ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets”. Journal of Artificial Intelligence Research (JAIR)  57, pp. 593–620, 2016. [pdf] [code]

  76. Guansong Pang, Huidong Jin, and Shengyi Jiang. “CenKNN: a scalable and effective text classifier”. Data Mining and Knowledge Discovery (DMKD) 29.3, pp. 593–625, 2015.

  77. Guansong Pang and Shengyi Jiang. “A generalized cluster centroid based classifier for text categorization”. Information Processing & Management (IP&M) 49.2, pp. 576–586, 2013.

  78. Shengyi Jiang, Guansong Pang, Meiling Wu, and Limin Kuang. “An improved K-nearest-neighbor algorithm for text categorization”. Expert Systems with Applications (ESWA) 39.1, pp. 1503–1509, 2012.

Workshops, Posters, and Abstracts

• 1. Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, and Gustavo Carneiro. "Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder." In: the 14th Workshop on Machine Learning in Medical Imaging (MICCAI 2023 workshop). 2023. [pdf]

  2. Qing Li, Xiao Huang, Ninghao Liu, Yuxiao Dong, and Guansong Pang. "International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning." In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM'23), pp. 1273-1274. 2023.

  3. Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, and Thomas G. Dietterich. "ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation." In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'22), pp. 4892-4893. 2022.

  4. Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, and Thomas G. Dietterich. "Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)." In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'21), pp. 4145-4146. 2021.

  5. Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, and Yongjun Wang. "DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities." In: PAKDD'21 Alibaba Cloud AIOps Competition. Best Paper Award. [pdf]

  6. Guansong Pang, Kai Ming Ting, and David Albrecht. “LeSiNN: Detecting anomalies   by identifying Least Similar Nearest Neighbours”. In: 2015 IEEE 15th International   Conference on Data Mining Workshops (ICDMW'15). IEEE, pp. 623–630, 2015.

  7. Guansong Pang, Huidong Jin, and Shengyi Jiang. “An effective class-centroid-based dimension reduction method for text classification”. In: Proceedings of the 22nd International Conference on World Wide Web (Companion Volume) (WWW'13). pp. 223–224, 2013.

Dissertations

1. Guansong Pang. "Non-IID outlier detection with coupled outlier factors", Thesis for Doctor of Philosophy , University of Technology Sydney, Australia, 2019 [pdf] (named on the prestigious UTS Chancellor's Award List)

2. Guansong Pang. "Anomaly detection based on zero appearances in subspaces", Thesis for Master of Philosophy, Monash University, Australia, 2015 [pdf] (First-class honors)