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arXiv · 2507.15156

Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification

Mykhailo Buleshnyi·Anna Polova·Zsolt Zombori·Michael Benedikt
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Abstract

We investigate multi-label classification involving large sets of labels, where the output labels may be known to satisfy some logical constraints. We look at an architecture in which classifiers for individual labels are fed into an expressive sequential model, which produces a joint distribution. One of the potential advantages for such an expressive model is its ability to modelling correlations, as can arise from constraints. We empirically demonstrate the ability of the architecture both to exploit constraints in training and to enforce constraints at inference time.

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Mykhailo Buleshnyi, Anna Polova, Zsolt Zombori, Michael Benedikt. 2025-07-20. Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification. https://arxiv.org/abs/2507.15156

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