Tabular: a schema-driven probabilistic programming language
2014 (English)In: Proc. 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, New York: ACM Press, 2014, 321-334 p.Conference paper (Refereed)
We propose a new kind of probabilistic programming language for machine learning. We write programs simply by annotating existing relational schemas with probabilistic model expressions. We describe a detailed design of our language, Tabular, complete with formal semantics and type system. A rich series of examples illustrates the expressiveness of Tabular. We report an implementation, and show evidence of the succinctness of our notation relative to current best practice. Finally, we describe and verify a transformation of Tabular schemas so as to predict missing values in a concrete database. The ability to query for missing values provides a uniform interface to a wide variety of tasks, including classification, clustering, recommendation, and ranking.
Place, publisher, year, edition, pages
New York: ACM Press, 2014. 321-334 p.
, ACM SIGPLAN NOTICES, ISSN 0362-1340 ; 49:1
Bayesian reasoning; machine learning; model-learner pattern; probabilistic programming; relational data
Research subject Computer Science
IdentifiersURN: urn:nbn:se:uu:diva-220064DOI: 10.1145/2535838.2535850ISI: 000331120500028ISBN: 9781450325448OAI: oai:DiVA.org:uu-220064DiVA: diva2:703977
POPL 2014, January 22–24, San Diego, CA
FunderSwedish Research Council, 2013-4853