{"type":"ScholarlyArticle","title":"Lifted Message Passing for Hybrid Probabilistic Inference","authors":[{"name":"Yuqiao Chen","slug":"yuqiao-chen","given":"Yuqiao","family":"Chen","affiliation":"University of Texas at Dallas"},{"name":"Nicholas Ruozzi","slug":"nicholas-ruozzi","given":"Nicholas","family":"Ruozzi","affiliation":"University of Texas at Dallas"},{"name":"Sriraam Natarajan","slug":"sriraam-natarajan","given":"Sriraam","family":"Natarajan","affiliation":"University of Texas at Dallas"}],"abstract":"Lifted inference algorithms for first-order logic models, e.g., Markov logic networks (MLNs), have been of significant interest in recent years.  Lifted inference methods exploit model symmetries in order to reduce the size of the model and, consequently, the computational cost of inference.  In this work, we consider the problem of lifted inference in MLNs with continuous or both discrete and continuous groundings. Existing work on lifting with continuous groundings has mostly been limited to special classes of models, e.g., Gaussian models, for which variable elimination or message-passing updates can be computed exactly.  Here, we develop approximate lifted inference schemes based on particle sampling.  We demonstrate empirically that our approximate lifting schemes perform comparably to existing state-of-the-art for models for Gaussian MLNs, while having the flexibility to be applied to models with arbitrary potential functions.","doi":"10.24963/ijcai.2019/790","url":"https://ijcai.org/proceedings/2019/790","pdf":"https://ijcai.org/proceedings/2019/0790.pdf","markdown":"https://ijcai.org/proceedings/2019/790.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/790","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Uncertainty in AI","track":"Main track","number":790,"pages":{"first":5701,"last":5707},"keywords":["Uncertainty in AI: Relational Inference","Uncertainty in AI: Uncertainty in AI","Uncertainty in AI: Approximate Probabilistic Inference"],"videos":[],"references":[{"position":1,"raw":"[Ahmadi et al., 2011] Babak Ahmadi, Kristian Kersting, and Scott Sanner. 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