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