Computer Science > Machine Learning
arXiv:2603.09563 (cs)
[Submitted on 10 Mar 2026]
Title:Learning Bayesian and Markov Networks with an Unreliable Oracle
View a PDF of the paper titled Learning Bayesian and Markov Networks with an Unreliable Oracle, by Juha Harviainen and Pekka Parviainen and Vidya Sagar Sharma
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Abstract:We study constraint-based structure learning of Markov networks and Bayesian networks in the presence of an unreliable conditional independence oracle that makes at most a bounded number of errors. For Markov networks, we observe that a low maximum number of vertex-wise disjoint paths implies that the structure is uniquely identifiable even if the number of errors is (moderately) exponential in the number of vertices. For Bayesian networks, however, we prove that one cannot tolerate any errors to always identify the structure even when many commonly used graph parameters like treewidth are bounded. Finally, we give algorithms for structure learning when the structure is uniquely identifiable.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09563 [cs.LG] |
| (or arXiv:2603.09563v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09563
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View a PDF of the paper titled Learning Bayesian and Markov Networks with an Unreliable Oracle, by Juha Harviainen and Pekka Parviainen and Vidya Sagar Sharma
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