Distance-based Learning of Hypertrees
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918219316461568 |
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| author | Fallat, Shaun Khodamoradi, Kamyar Kirkpatrick, David Maliuk, Valerii Mojallal, S. Ahmad Zilles, Sandra |
| author_facet | Fallat, Shaun Khodamoradi, Kamyar Kirkpatrick, David Maliuk, Valerii Mojallal, S. Ahmad Zilles, Sandra |
| contents | We study the problem of learning hypergraphs with shortest-path queries (SP-queries), and present the first provably optimal online algorithm for a broad and natural class of hypertrees that we call orderly hypertrees. Our online algorithm can be transformed into a provably optimal offline algorithm. Orderly hypertrees can be positioned within the Fagin hierarchy of acyclic hypergraph (well-studied in database theory), and strictly encompass the broadest class in this hierarchy that is learnable with subquadratic SP-query complexity.
Recognizing that in some contexts, such as evolutionary tree reconstruction, distance measurements can degrade with increased distance, we also consider a learning model that uses bounded distance queries. In this model, we demonstrate asymptotically tight complexity bounds for learning general hypertrees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distance-based Learning of Hypertrees Fallat, Shaun Khodamoradi, Kamyar Kirkpatrick, David Maliuk, Valerii Mojallal, S. Ahmad Zilles, Sandra Machine Learning We study the problem of learning hypergraphs with shortest-path queries (SP-queries), and present the first provably optimal online algorithm for a broad and natural class of hypertrees that we call orderly hypertrees. Our online algorithm can be transformed into a provably optimal offline algorithm. Orderly hypertrees can be positioned within the Fagin hierarchy of acyclic hypergraph (well-studied in database theory), and strictly encompass the broadest class in this hierarchy that is learnable with subquadratic SP-query complexity. Recognizing that in some contexts, such as evolutionary tree reconstruction, distance measurements can degrade with increased distance, we also consider a learning model that uses bounded distance queries. In this model, we demonstrate asymptotically tight complexity bounds for learning general hypertrees. |
| title | Distance-based Learning of Hypertrees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.22014 |