Declarative experimentation for machine learning.

With this project we introduce the concept of "declarative experimentation". The motivation for declarative experimentation is that empirical testing of a hypothesis currently demands a lot from the scientist, in terms of setting up experiments, analyzing the results, and ensuring all this is done correctly. This process is not only labor-intensive but also error-prone. These problems would be solved if a scientist could formulate a hypothesis as a query in some formal language, after which the remainder of the experimental process would be performed automatically. This is somewhat similar to how people query databases by formulating questions declaratively, instead of programming the retrieval procedure themselves; hence the name "declarative experimentation". While it may not be realistic to assume that declarative experimentation is possible in all sciences, we are convinced it is possible and desirable in the area of machine learning, and likely also in many other experimental sciences, including bioinformatics. In this project, building on the already existing concept of experiment databases, we will develop a theory and methods for declarative experimentation in machine learning. The results may serve as a starting point for developing declarative experimentation in other areas.

Keywords:Declarative languages, Experiment databases, Declarative experimentation, Computer science, Machine learning

Disciplines:Artificial intelligence, Cognitive science and intelligent systems, Applied mathematics in specific fields