Key Publications

Here are only key publications. Please see my Google scholar or Deakin website for the full list.

Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu (2023). Towards a Persistence Diagram that is Robust to Noise and Varied Densities. The 40th International Conference on Machine Learning (ICML-23).

Project

Ye Zhu, Kai Ming Ting (2023). Kernel-based clustering via Isolation Distributional Kernel. Information Systems.

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Ye Zhu, Kai Ming Ting (2022). A new distributional treatment for time series and an anomaly detection investigation. The 48th International Conference on Very Large Databases (VLDB-22).

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Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li (2022). Streaming Hierarchical Clustering Based on Point-Set Kernel. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-22).

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Ye Zhu, Kai Ming Ting (2022). Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel. The Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22).

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Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova (2021). Hierarchical clustering that takes advantage of both density-peak and density-connectivity. Information Systems.

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Ye Zhu, Kai Ming Ting (2021). Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel. Journal of Artificial Intelligence Research.

PDF Code Project DOI

Ye Zhu, Kai Ming Ting, Mark Carman, Maia Angelova (2021). CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognition.

PDF Code Project DOI

Zhao Wenjie, Zhang Yushu, Ye Zhu, Xu Peng (2021). Anomaly detection of aircraft lead-acid battery. Quality and Reliability Engineering International.

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Mathew Zuparic, Maia Angelova, Ye Zhu, Alexander Kalloniatis (2021). Adversarial decision strategies in multiple network phased oscillators: the Blue-Green-Red Kuramoto-Sakaguchi model. Communications in Nonlinear Science and Numerical Simulation.

DOI

Yuan Jin, Mark Carman, Ye Zhu, Yong Xiang (2020). A technical survey on statistical modelling and design methods for crowdsourcing quality control. Artificial Intelligence.

DOI

Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent CS Lee (2019). Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).

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Yushu Zhang, Jin Jiang, Yong Xiang, Ye Zhu, Liangtian Wan, Xiyuan Xie (2018). Cloud-assisted privacy-conscious large-scale Markowitz portfolio. Information Sciences.

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Kai Ming Ting, Ye Zhu, Mark Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou (2018). Lowest probabilitymass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. Machine Learning.

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Yuan Jin, Lan Du, Ye Zhu, Mark Carman (2018). Leveraging label category relationships in multi-class crowdsourcing. Advances in Knowledge Discovery and Data Mining (PAKDD 2018).

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Ye Zhu, Kai Ming Ting, Maia Angelova (2018). A Distance Scaling Method to Improve Density-Based Clustering. Advances in Knowledge Discovery and Data Mining (PAKDD 2018).

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Ye Zhu, Kai Ming Ting, Mark Carman (2018). Grouping Points by Shared Subspaces for Effective Subspace Clustering. Pattern Recognition.

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Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu (2018). Anomaly detection of aircraft lead-acid battery. Machine Learning.

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Tharindu R Bandaragoda, Kai Ming Ting, David Albrecht, Fei Tony Liu, Ye Zhu, Jonathan R Wells (2018). Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence.

Code Project DOI

Kai Ming Ting, Ye Zhu, Mark Carman, Yue Zhu, Zhi-Hua Zhou (2016). Overcoming key weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

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Ye Zhu, Kai Ming Ting, Mark Carman (2016). Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognition.

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