Presentation Title

Transfer Learning Capable Symbolic Regression

Presenter's Name(s)

Ryan GrindleFollow

Project Collaborators

Jim P. Bagrow (Advisor), Josh Bongard (Advisor)

Abstract

The ever-growing accumulation of data makes automated distillation of understandable models from that data ever-more desirable. Deriving equations directly from data using symbolic regression, as performed by genetic programming, continues its appeal due to its algorithmic simplicity and lack of assumptions about equation form. But, genetic programming has not yet been shown capable of transfer learning: the ability to rapidly distill equations successfully on new data from a previously-unseen domain, due to experience performing this distillation on other domains. Given neural networks' proven ability to transfer learn, here we introduce a neural architecture that, after training, iteratively rewrites an inaccurate equation given its current error, regardless of the domain. We found that trained networks can improve their ability to derive equations from data produced by a test domain, when trained on data from several training domains. Although this phenomenon did not arise in all cases we tested, it does suggest that symbolic regression can more rapidly distill equations from data if exposed to data from a growing set of domains.

Primary Faculty Mentor Name

Jim Bagrow

Secondary Mentor NetID

jbongard

Secondary Mentor Name

Josh Bongard

Status

Graduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Mathematical Sciences

Primary Research Category

Engineering & Physical Sciences

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Transfer Learning Capable Symbolic Regression

The ever-growing accumulation of data makes automated distillation of understandable models from that data ever-more desirable. Deriving equations directly from data using symbolic regression, as performed by genetic programming, continues its appeal due to its algorithmic simplicity and lack of assumptions about equation form. But, genetic programming has not yet been shown capable of transfer learning: the ability to rapidly distill equations successfully on new data from a previously-unseen domain, due to experience performing this distillation on other domains. Given neural networks' proven ability to transfer learn, here we introduce a neural architecture that, after training, iteratively rewrites an inaccurate equation given its current error, regardless of the domain. We found that trained networks can improve their ability to derive equations from data produced by a test domain, when trained on data from several training domains. Although this phenomenon did not arise in all cases we tested, it does suggest that symbolic regression can more rapidly distill equations from data if exposed to data from a growing set of domains.