Learning Probabilistic Logic Programs over Continuous Data

Stefanie Speichert, Vaishak Belle

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

Abstract / Description of output

The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming (PLP): the enabling of stochastic primitives in logic programming. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductive logic programming and related techniques have paved much of the way for the last few decades, but a major limitation of this exciting landscape is that only discrete features and distributions are handled. Many disciplines express phenomena in terms of continuous models. In this paper, we propose a new computational framework for inducing probabilistic logic programs over continuous and mixed discrete-continuous data. Most significantly, we show how to learn these programs while making no assumption about the true underlying density. Our experiments show the promise of the proposed framework.
Original languageEnglish
Title of host publicationInductive Logic Programming
Subtitle of host publication29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3–5, 2019, Proceedings
EditorsDimitar Kazakov, Can Erten
PublisherSpringer, Cham
Number of pages16
ISBN (Electronic)978-3-030-49210-6
ISBN (Print)978-3-030-49209-0
Publication statusPublished - 5 Jun 2020
Event29th International Conference on Inductive Logic Programming - Plovdiv, Bulgaria
Duration: 3 Sept 20195 Sept 2019

Publication series

Name Lecture Notes in Computer Scienc
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Inductive Logic Programming
Abbreviated titleILP 2019
Internet address


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