Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2535838.2535850 | POPL |
Keywords | Field | DocType |
new kind,existing relational schema,detailed design,probabilistic programming language,machine learning,probabilistic model expression,missing value,schema-driven probabilistic programming language,current best practice,concrete database,formal semantics,relational data,computer science,bayesian reasoning | Notation,Programming language,Expression (mathematics),Ranking,Relational database,Succinctness,Computer science,Probabilistic programming language,Missing data,Cluster analysis | Conference |
Volume | Issue | ISSN |
49 | 1 | 0362-1340 |
Citations | PageRank | References |
21 | 0.89 | 22 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Gordon | 1 | 3713 | 268.70 |
| Thore Graepel | 2 | 4211 | 242.71 |
| Nicolas Rolland | 3 | 26 | 1.94 |
| Claudio Russo | 4 | 89 | 5.33 |
| Johannes Borgstrom | 5 | 46 | 2.65 |
| John Guiver | 6 | 482 | 21.48 |