Learning Credal Sum-Product Networks

Amelie Levray, Vaishak Belle

Research output: Contribution to conferencePaperpeer-review


Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support efficient probabilistic querying. By leveraging local structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries tobe computable in time polynomial in the network size. While the progress is impressive, numerous data  sources are incomplete, and in the presence of missing data, structure learning methods nonetheless revert to single distributions without characterizing the loss in confidence. In recent work, credal sum-product networks, an imprecise extension of sum-product networks, were proposed to capture this robustness angle. In this work, we are interested in how such representations can be learnt and thus study how the computational machinery underlying tractable learning and inference can be generalized for imprecise probabilities.
Original languageEnglish
Number of pages24
Publication statusPublished - 22 Jun 2020
Event2nd Conference on Automated Knowledge Base Construction 2020 - Online conference
Duration: 22 Jun 202024 Jun 2020
Conference number: 2


Conference2nd Conference on Automated Knowledge Base Construction 2020
Abbreviated titleAKBC 2020
Internet address


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