← Back to all news
arXiv Machine Learning By Chen Yang, Changhao Zhao, Haoyang Zhao, Youquan He, Chen Wang, Jiansheng Fan

Leakage-Free Evaluation and Distribution-Robust Spatio-Temporal Graph Learning for Inductive Kriging

Read the original on arXiv Machine Learning →

The paper introduces a leakage‑free 3×3 spatio‑temporal partition for evaluating inductive kriging, ensuring training, validation, and testing occur on distinct spatial and temporal domains. It proposes DRIK, a framework that includes Spatial Continuity Regularization, Masked Flow Disambiguation, and Structural Domain Expansion to mitigate structural shifts from unseen nodes. Experiments on six datasets show DRIK outperforms existing baselines, reducing MAE by up to 12.48% and achieving lower test‑to‑validation MAE ratios under the stricter evaluation protocol.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 26

Multi-Source Complex Network Reconstruction via Wasserstein Distributionally Robust Optimization and Algorithm Unrolling

The paper introduces MS‑WDRO, a multi‑source Wasserstein distributionally robust optimization framework for reconstructing complex network topologies from scarce target‑domain data and abundant heterogeneous source data. It fuses sources via a weighted Wasserstein barycenter, builds an ambiguity set around it, and solves a regularized Laplacian estimator using a provably convergent ADMM scheme. The authors provide finite‑sample guarantees, demonstrate that naive aggregation is suboptimal, and show through experiments on synthetic data and the ABIDE I neuroimaging dataset that MS‑WDRO outperforms seven baselines in graph recovery, sample efficiency, and diagnostic utility, especially when target samples are limited.

By Chuansen Peng, Yifan Xia, Jinshan Zhong, Xiaojing Shen
efficiencybenchmarkssafety
More like this →