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Jason H. Moore

3 accepted papers

2021

Contemporary Symbolic Regression Methods and their Relative Performance

NeurIPS 2021poster

Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. In this paper, we address this shortcoming by introducing an open-source, reproducible benchmark…

Cited by 418SourceScholar
2019

Learning concise representations for regression by evolving networks of trees

ICLR 2019poster

We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions. Differentiable features are…

Cited by 75SourcePDFScholar
2017

Network-based genome wide study of hippocampal imaging phenotype in Alzheimer's Disease to identify functional interaction modules

ICASSP 2017accepted

Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majo…

Cited by 0SourceScholar