Guoyin Wang

Advancing Zero-Shot Adversarial Robust Fairness: Theory and Algorithm.

An Efficient Feature Selection Method Using the Granular-Ball Divergence-Based Fuzzy Rough Hypergraph

Granular-Ball Computing (GBC) is an efficient, robust, and highly interpretable multi-granularity representation and computation method. Nonetheless, most feature selection methods based on GBC require considerable time to calculate the significance measures of features or repeatedly generate granular balls, which limi...

Multi-Granularity Graph Contrastive Learning Framework via Granular-Ball on Heterogeneous Graphs.

Graph Contrastive Learning (GCL) is a popular self-supervised learning (SSL) technique. However, mainstream GCL methods usually favor single fine-grained random augmentation schemes, which will destroy the structural integrity of the graph, and they largely ignore the topology of the graph structure, that is, multi-gra...

Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning

Granular-Ball Quantum Clustering (GBQC) is proposed, a framework that tightly couples granular-ball structural abstraction with quantum feature learning, and significantly reduces quantum kernel evaluations and computational overhead, enabling quantum clustering experiments on larger datasets within parameterized quant...

Adaptive $k$ Nearest Neighbors Classifier via Granular Ball Computing

Experimental results demonstrate that the proposed adaptive and efficient KNN approach via granular-ball computing outperforms existing KNN variants across multiple datasets in terms of both accuracy and efficiency.