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

Fuente: arXiv
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Main Authors: Buleshnyi, Mykhailo, Polova, Anna, Zombori, Zsolt, Benedikt, Michael
Format: Preprint
Published: 2025
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author Buleshnyi, Mykhailo
Polova, Anna
Zombori, Zsolt
Benedikt, Michael
author_facet Buleshnyi, Mykhailo
Polova, Anna
Zombori, Zsolt
Benedikt, Michael
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2507_15156
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification
Buleshnyi, Mykhailo
Polova, Anna
Zombori, Zsolt
Benedikt, Michael
Machine Learning
Artificial Intelligence
Logic in Computer Science
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.
title Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification
topic Machine Learning
Artificial Intelligence
Logic in Computer Science
url https://arxiv.org/abs/2507.15156