ReLaG: A Scalable Framework Generalizing Random Splits to Data with Latent Relations

Abstract:Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as biochemical studies. This leads to overly optimistic generalization estimates. Here, we introduce ReLaG, a modality-agnostic framework that models sample relatedness through a hierarchical latent-variable process and infers groups of related samples using proximity graphs and community detection to produce independent train--test subsets. Across molecular and protein datasets, ReLaG matches existing relation-aware methods while scaling substantially better, enabling splits at previously impractical dataset sizes. We further introduce a label-free procedure that adapts the splitting resolution to production data, aligning evaluation with the intended deployment setting. ReLaG's inferred groups provide a cheap estimate of effective dataset size, enabling diversity-aware dataset scaling. ReLaG is open source and can be installed with pip install relag.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.38538 [cs.LG]

(or arXiv:2609.38538v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.38538 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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