Optimization Framework with Minimum Description Length Principle for Probabilistic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  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