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Tabular : a schema-driven probabilistic programming language
- Gordon, Andrew D. (author)
- Graepel, Thore (author)
- Rolland, Nicolas (author)
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- Russo, Claudio (author)
- Guiver, John (author)
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- (creator_code:org_t)
- 2014-01-08
- 2014
- English.
- In: Proc. 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. - New York : ACM Press. - 9781450325448 ; , s. 321-334
- Conference paper (peer-reviewed)
Abstract
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Bayesian reasoning; machine learning; model-learner pattern; probabilistic programming; relational data
- Datavetenskap
- Computer Science
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- ref (subject category)
- kon (subject category)
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- Uppsala University
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