Optimization Framework with Minimum Description Length Principle for Probabilistic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Potapov:2015:AGI,

• author = "Alexey Potapov and Vita Batishcheva and Sergey Rodionov",
• title = "Optimization Framework with Minimum Description Length Principle for Probabilistic Programming",
• booktitle = "Artificial General Intelligence",
• year = "2015",
• editor = "Jordi Bieger and Ben Goertzel and Alexey Potapov",
• volume = "9205",
• series = "LNCS",
• pages = "331--340",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Probabilistic programming, MDL, Image interpretation, AGI",
• isbn13 = "978-3-319-21365-1",
• URL = "https://link.springer.com/book/10.1007%2F978-3-319-21365-1",
• DOI = "doi:10.1007/978-3-319-21365-1_34",
• abstract = "Application of the Minimum Description Length principle to optimization queries in probabilistic programming was investigated on the example of the C++ probabilistic programming library under development. It was shown that incorporation of this criterion is essential for optimization queries to behave similarly to more common queries performing sampling in accordance with posterior distributions and automatically implementing the Bayesian Occam's razor. Experimental validation was conducted on the task of blood cell detection on microscopic images. Detection appeared to be possible using genetic programming query, and automatic penalization of candidate solution complexity allowed to choose the number of cells correctly avoiding overfitting.",

}

Genetic Programming entries for Alexey Potapov Vita Batishcheva Sergey Rodionov

Citations