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Author withdraws arXiv paper on surrogate graph partitioning for spatial prediction

By PulseAugur Editorial · [1 sources] ·

A research paper titled "Surrogate Graph Partitioning for Spatial Prediction" was submitted to arXiv, proposing a graph partitioning method to create interpretable spatial segments for prediction tasks. The paper aimed to address the computational complexity of exact solutions by developing an approximation scheme. However, the paper has since been withdrawn by its author, Yuta Shikuri. AI

IMPACT This withdrawn research on interpretable spatial prediction models has no direct industry impact.

RANK_REASON The item is a withdrawn academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Author withdraws arXiv paper on surrogate graph partitioning for spatial prediction

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The item is a withdrawn academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yuta Shikuri, Hironori Fujisawa ·

    Surrogate Graph Partitioning for Spatial Prediction

    arXiv:2510.07832v2 Announce Type: replace Abstract: Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improved the capacity to model diverse observation types, adoption in practice remains limit…

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