Learning Shuffle Ideals with Membership Queries and Contrastive Examples

Abstract:This paper studies learning of shuffle ideals with membership queries as well as with contrastive queries---a form of membership query that reveals not only whether a selected word $w$ is in the target language or not, but also provides a most similar word $w'$ that belongs to the target language if %and only if $w$ does not. For both settings, it is shown that even some very simple classes of shuffle ideals cannot be learned efficiently. By contrast, we obtain positive learnability results for classes of shuffle ideals that meet certain structural conditions. In the case of membership queries, these structural conditions are related to the previously studied notion of universal words, and raise new questions in word combinatorics. In the case of contrastive queries, the structural conditions relate to partitioning sets of words that are listed in shortlex order.

Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG)

arXiv:2609.32820 [cs.FL]

(or arXiv:2609.32820v1 [cs.FL] for this version)

https://doi.org/10.48550/arXiv.2609.32820 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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