Author

Jacques Corbeil

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#machine learning Preprint Oct 2026

Revisiting Explainable AI through Model-Independent Concept Dictionaries

Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transparency. However, existing XAI methods typically assume input features to be inherently interpretable, or they rely on intermediate internal abstractions that are difficul...

T. Schnake, Doreen Schöppenthau, Alexander Meyer et al. · 0 citations

#artificial intelligence Preprint Sep 2026

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 sample relatedness throu...

Anthony Lavertu, Jacob A. Cote, S. Gobeil et al. · 0 citations