| 2026 | AAAI | Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier. | Xinpeng Li, Kai Ming Ting |
| 2026 | AAAI | IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection. | Yang Xu, Yixiao Ma, Kaifeng Zhang, Zuliang Yang, Kai Ming Ting |
| 2026 | AAAI | GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing. | Yang Xu, Zuliang Yang, Kai Ming Ting |
| 2026 | AAAI | SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection. | Yang Xu, Hang Zhang, Yixiao Ma, Ye Zhu, Kai Ming Ting |
| 2026 | ACL | LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval. | Zhibo Zhang, Yang Xu, Kai Ming Ting, Cam-Tu Nguyen |
| 2026 | ICCBR | Case-Based Interpretability in Graph-Level Anomaly Detection via Contrast with Normal Prototypes. | Qiuran Zhao, Kai Ming Ting, Xinpeng Li |
| 2026 | KSEM | Online Automatic Modulation Classification Based on Distributional Signal Representation. | Xinpeng Li, Zile Jiang, Kai Ming Ting, Ye Zhu |
| 2025 | CIKM | Contrastive Multi-View Graph Hashing. | Yang Xu, Zuliang Yang, Kai Ming Ting |
| 2025 | KSEM | Streaming Hierarchical Clustering for Emerging New Class. | Yixiao Ma, Ye Zhu, Yang Xu, Kai Ming Ting |
| 2024 | IJCAI | Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint). | Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li |
| 2024 | PAKDD | Local Subsequence-Based Distribution for Time Series Clustering. | Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, Ye Zhu |
| 2024 | PAKDD | Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. | Yuanyi Shang, Kai Ming Ting, Zijing Wang, Yufan Wang |
| 2023 | ICDM | Distribution-Based Trajectory Clustering. | Zijing Wang, Ye Zhu, Kai Ming Ting |
| 2023 | ICML | Towards a Persistence Diagram that is Robust to Noise and Varied Densities. | Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu |
| 2023 | SDM | Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. | Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song |
| 2022 | IJCAI | Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract). | Ye Zhu, Kai Ming Ting |
| 2022 | KDD | Streaming Hierarchical Clustering Based on Point-Set Kernel. | Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li |
| 2021 | AAAI | Isolation Graph Kernel. | Bi-Cun Xu, Kai Ming Ting, Yuan Jiang |
| 2021 | ICDM | Isolation Kernel Density Estimation. | Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang |
| 2021 | SDM | Reconstruction-based Anomaly Detection with Completely Random Forest. | Yi-Xuan Xu, Ming Pang, Ji Feng, Kai Ming Ting, Yuan Jiang, Zhi-Hua Zhou |
| 2020 | KDD | Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection. | Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou |
| 2020 | PAKDD | Anomaly Detection via Neighbourhood Contrast. | Bo Chen, Kai Ming Ting, Tat-Jun Chin |
| 2020 | WISE | A New Effective and Efficient Measure for Outlying Aspect Mining. | Durgesh Samariya, Sunil Aryal, Kai Ming Ting, Jiangang Ma |
| 2019 | AAAI | Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. | Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee |
| 2019 | KDD | Isolation Set-Kernel and Its Application to Multi-Instance Learning. | Bi-Cun Xu, Kai Ming Ting, Zhi-Hua Zhou |
| 2018 | ICDM | Which Outlier Detector Should I use? | Kai Ming Ting, Sunil Aryal, Takashi Washio |
| 2018 | KDD | Isolation Kernel and Its Effect on SVM. | Kai Ming Ting, Yue Zhu, Zhi-Hua Zhou |
| 2018 | PAKDD | Neighbourhood Contrast: A Better Means to Detect Clusters Than Density. | Bo Chen, Kai Ming Ting |
| 2018 | PAKDD | A Distance Scaling Method to Improve Density-Based Clustering. | Ye Zhu, Kai Ming Ting, Maia Angelova |
| 2017 | AAAI | Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. | Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou |
| 2017 | ICDM | New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning. | Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou |
| 2016 | ICDM | Multi-label Learning with Emerging New Labels. | Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou |
| 2016 | KDD | Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. | Kai Ming Ting, Ye Zhu, Mark James Carman, Yue Zhu, Zhi-Hua Zhou |
| 2015 | ICDM | LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. | Guansong Pang, Kai Ming Ting, David W. Albrecht |
| 2014 | ICDM | Mp-Dissimilarity: A Data Dependent Dissimilarity Measure. | Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio |
| 2014 | ICDM | Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble. | Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Jonathan R. Wells |
| 2014 | PAKDD | Improving iForest with Relative Mass. | Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio |
| 2013 | IJCAI | Optimizing Cepstral Features for Audio Classification. | Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang |
| 2013 | PAKDD | MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation. | Sunil Aryal, Kai Ming Ting |
| 2012 | AusDM | A non-time series approach to vehicle related time series problems. | Jonathan R. Wells, Kai Ming Ting, Naiwala P. Chandrasiri |
| 2012 | SSPR | Learning Sparse Kernel Classifiers in the Primal. | Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang |
| 2011 | ICDM | Density Estimation Based on Mass. | Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu |
| 2011 | ICONIP | On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification. | Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang |
| 2011 | IJCAI | Fast Anomaly Detection for Streaming Data. | Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu |
| 2010 | ICDM | Multi-dimensional Mass Estimation and Mass-based Clustering. | Kai Ming Ting, Jonathan R. Wells |
| 2010 | ICPR | Learning Naive Bayes Classifiers for Music Classification and Retrieval. | Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang |
| 2010 | KDD | Mass estimation and its applications. | Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, James Swee Chuan Tan |
| 2010 | SSPR | On Feature Combination for Music Classification. | Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang |
| 2008 | CEC | Issues of grid-cluster retrievals in swarm-based clustering. | Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng |
| 2008 | ICDM | Isolation Forest. | Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou |
| 2007 | ICDM | Cocktail Ensemble for Regression. | Yang Yu, Zhi-Hua Zhou, Kai Ming Ting |
| 2006 | CEC | Reproducing the Results of Ant-based Clustering Without Using Ants. | Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng |
| 2006 | PAKDD | Variable Randomness in Decision Tree Ensembles. | Fei Tony Liu, Kai Ming Ting |
| 2005 | PAKDD | Maximizing Tree Diversity by Building Complete-Random Decision Trees. | Fei Tony Liu, Kai Ming Ting, Wei Fan |
| 2003 | ICDM | Model Stability: A key factor in determining whether an algorithm produces an optimal model from a matching distribution. | Kai Ming Ting, Regina Jing Ying Quek |
| 2002 | DIS | A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. | Kai Ming Ting |
| 2002 | ICML | Issues in Classifier Evaluation using Optimal Cost Curves. | Kai Ming Ting |
| 2000 | ICML | A Comparative Study of Cost-Sensitive Boosting Algorithms. | Kai Ming Ting |
| 1999 | ICML | Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. | Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting |
| 1999 | IJCNN | A fuzzy neural network for data mining: dealing with the problem of small disjuncts. | Yakov Frayman, Kai Ming Ting, Lipo Wang |
| 1999 | PAKDD | Improving the Performance of Boosting for Naive Bayesian Classification. | Kai Ming Ting, Zijian Zheng |
| 1998 | DIS | Boosting Cost-Sensitive Trees. | Kai Ming Ting, Zijian Zheng |
| 1998 | ICTAI | Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning. | Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting |
| 1997 | ICML | Stacking Bagged and Dagged Models. | Kai Ming Ting, Ian H. Witten |
| 1997 | IJCAI | Stacked Generalizations: When Does It Work? | Kai Ming Ting, Ian H. Witten |
| 1996 | ICML | The Characterisation of Predictive Accuracy and Decision Combination. | Kai Ming Ting |
| 1995 | ICCBR | Towards using a Single Uniform Metric in Instance-Based Learning. | Kai Ming Ting |