Optimization Framework with Minimum Description Length Principle for Probabilistic Programming
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- @InProceedings{Potapov:2015:AGI,
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author = "Alexey Potapov and Vita Batishcheva and
Sergey Rodionov",
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title = "Optimization Framework with Minimum Description Length
Principle for Probabilistic Programming",
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booktitle = "Artificial General Intelligence",
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year = "2015",
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editor = "Jordi Bieger and Ben Goertzel and Alexey Potapov",
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volume = "9205",
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series = "LNCS",
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pages = "331--340",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Probabilistic
programming, MDL, Image interpretation, AGI",
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isbn13 = "978-3-319-21365-1",
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URL = "https://link.springer.com/book/10.1007%2F978-3-319-21365-1",
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DOI = "doi:10.1007/978-3-319-21365-1_34",
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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