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20232025
most citedTwo-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling

1 citations · 2 across the 5 of their papers we have counts for

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cs.LG2025★ 1 cited

SHAP Distance: An Explainability-Aware Metric for Evaluating the Semantic Fidelity of Synthetic Tabular Data

Ke Yu, Shigeru Ishikura, Yukari Usukura +2

Synthetic tabular data, which are widely used in domains such as healthcare, enterprise operations, and customer analytics, are increasingly evaluated to ensure that they preserve…

cs.LG2025

GraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generation

Xinrui Li, Qilin Fan, Tianfu Wang +3

Federated graph learning (FGL) enables multiple clients to collaboratively train powerful graph neural networks without sharing their private, decentralized graph data. Inherited f…