Zhongxi Fang — accepted AI-conference papers

2025

StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional Scaling

Zhongxi Fang, Xun Su, Tomohisa Tabuchi, Jianming Huang, Hiroyuki Kasai

AISTATS 2025poster

Multidimensional Scaling (MDS) is an essential technique in multivariate analysis, with Weighted MDS (WMDS) commonly employed for tasks such as dimensionality reduction and graph drawing. However, the optimization of WMDS poses significant challenges due to the highly non-convex nature of its object…

Cited by 0 Source ↗Scholar ↗

2023

Wasserstein Graph Distance Based on L1–Approximated Tree Edit Distance between Weisfeiler–Lehman Subtrees

Zhongxi Fang, Jianming Huang, Xun Su, Hiroyuki Kasai

AAAI 2023technical

The Weisfeiler-Lehman (WL) test is a widely used algorithm in graph machine learning, including graph kernels, graph metrics, and graph neural networks. However, it focuses only on the consistency of the graph, which means that it is unable to detect slight structural differences. Consequently, this…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗