11 papers · 1 filter
Topologically Relevant Landmark Selection on Point Clouds
Kaifeng Zhang, Kai Ming Ting
Persistent Homology (PH) is an important tool in Topological Data Analysis for point clouds, but can be prohibitively expensive for large datasets. A practical approximation is to…
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
Hang Zhang, Kaifeng Zhang, Yixiao Ma +3
Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data $D_…
Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams
Kaifeng Zhang, Kai Ming Ting
Persistence Diagram (PD) is known to capture point cloud topology effectively, but its computation has high time complexity. Expected Persistence Diagram (EPD) has been developed t…
Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory
Kaifeng Zhang, Kai Ming Ting, Sanjay Chawla
Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point similarity function to find simil…
AGPO: Asymmetric Group Policy Optimization for Verifiable Reasoning and Search Ads Relevance at JD
Yang Xu, Kun Yao, Yiming Deng +3
Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated notable success in enhancing the reasoning performance of large language models (LLMs). However, recent studi…
Optimization-Free Graph Embedding via Distributional Kernel for Community Detection
Shuaibin Song, Kai Ming Ting, Kaifeng Zhang +1
Neighborhood Aggregation Strategy (NAS) is a widely used approach in graph embedding, underpinning both Graph Neural Networks (GNNs) and Weisfeiler-Lehman (WL) methods. However, NA…
How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning
Kai Ming Ting, Wei-Jie Xu, Hang Zhang
Deep clustering (DC) is often quoted to have a key advantage over -means clustering. Yet, this advantage is often demonstrated using image datasets only, and it is unclear wheth…
Rethinking Divisive Hierarchical Clustering from a Distributional Perspective
Kaifeng Zhang, Kai Ming Ting, Tianrun Liang +1
We uncover that current objective-based Divisive Hierarchical Clustering (DHC) methods produce a dendrogram that does not have three desired properties i.e., no unwarranted splitti…
Mass Distribution versus Density Distribution in the Context of Clustering
Kai Ming Ting, Ye Zhu, Hang Zhang +1
This paper investigates two fundamental descriptors of data, i.e., density distribution versus mass distribution, in the context of clustering. Density distribution has been the de…
A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data
Di Su, Kai Ming Ting, Jie Zhang +2
The detection of rare cell types in single-cell transcriptomics data is crucial for elucidating disease pathogenesis and tissue development dynamics. However, a critical gap that p…
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval
Zhibo Zhang, Yang Xu, Kai Ming Ting +1
Large language models (LLMs) have recently enabled remarkable progress in text representation. However, their embeddings are typically high-dimensional, leading to substantial stor…