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SUMMARY:Harry Desmond (University of Portsmouth)
DTSTART:20230511T160000Z
DTEND:20230511T170000Z
DTSTAMP:20260423T035414Z
UID:MPML/104
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/104/">E
 xhaustive Symbolic Regression (or how to find the best function for your d
 ata)</a>\nby Harry Desmond (University of Portsmouth) as part of Mathemati
 cs\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nSymbolic r
 egression aims to find optimal functional representation of datasets\, wit
 h broad applications across science. This is traditionally done using a "g
 enetic algorithm" which stochastically searches function space using an ev
 olution-inspired method for generating new trial functions. Motivated by t
 he uncertainties inherent in this approach -- and its failure on seemingly
  simple test cases -- I will describe a new method which exhaustively sear
 ches and evaluates function space. Coupled to a model selection principle 
 based on minimum description length\, Exhaustive Symbolic Regression is gu
 aranteed to find the simple equations that optimally balance simplicity wi
 th accuracy on any dataset. I will describe how the method works and showc
 ase it on Hubble rate measurements and dynamical galaxy data.\n\nBased on 
 work with Deaglan Bartlett and Pedro G. Ferreira: <br>\nhttps://arxiv.org/
 abs/2211.11461 <br>\nhttps://arxiv.org/abs/2301.04368\n
LOCATION:https://researchseminars.org/talk/MPML/104/
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