Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains

Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

While all kinds of mixed data---from personal data, over panel and scientific data, to public and commercial data---are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models. To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise polynomial leaf distributions together with novel nonparametric decomposition and conditioning steps using the Hirschfeld-Gebelein-Renyi Maximum Correlation Coefficient. This relieves the user from deciding a-priori the parametric form of the random variables but is still expressive enough to effectively approximate any distribution and permits efficient learning and inference.Our experiments show that the architecture, called Mixed SPNs, can indeed capture complex distributions across a wide range of hybrid domains.
Original languageEnglish
Title of host publicationThirty-Second AAAI Conference on Artificial Intelligence
Place of PublicationPalo Alto, California, USA
PublisherAAAI Press
Number of pages8
ISBN (Electronic)978-1-57735-800-8
Publication statusPublished - 7 Feb 2018
EventThirty-Second AAAI Conference on Artificial Intelligence - Hilton New Orleans Riverside, New Orleans, United States
Duration: 2 Feb 20187 Feb 2018

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


ConferenceThirty-Second AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2018
Country/TerritoryUnited States
CityNew Orleans
Internet address

Keywords / Materials (for Non-textual outputs)

  • sum-product networks
  • non-parametric density estimation
  • non-parametric independency test
  • hybrid domains
  • mixed graphical models


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