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Contemporary Symbolic Regression Methods and their Relative Performance


Jul 29, 2021
William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore

* To appear in Neurips 2021 Track on Datasets and Benchmarks. Main text: 10 pages, 3 figures; Appendix: 7 pages, 8 figures. https://openreview.net/forum?id=xVQMrDLyGst 

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PMLB v1.0: an open source dataset collection for benchmarking machine learning methods


Nov 30, 2020
Trang T. Le, William La Cava, Joseph D. Romano, John T. Gregg, Daniel J. Goldberg, Praneel Chakraborty, Natasha L. Ray, Daniel Himmelstein, Weixuan Fu, Jason H. Moore

* 6 pages, 2 figures 

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Genetic programming approaches to learning fair classifiers


Apr 28, 2020
William La Cava, Jason H. Moore

* 9 pages, 7 figures. GECCO 2020 

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Evaluating recommender systems for AI-driven data science


Jun 07, 2019
William La Cava, Heather Williams, Weixuan Fu, Jason H. Moore

* 14 pages, 6 figures 

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Epsilon-Lexicase Selection for Regression


May 30, 2019
William La Cava, Lee Spector, Kourosh Danai

* 9 pages, 9 figures. Presented at GECCO '16. Includes correction 

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Semantic variation operators for multidimensional genetic programming


Apr 18, 2019
William La Cava, Jason H. Moore

* 9 pages, 8 figures, GECCO 2019 

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Interpretation of machine learning predictions for patient outcomes in electronic health records


Mar 14, 2019
William La Cava, Christopher Bauer, Jason H. Moore, Sarah A Pendergrass

* 10 pages, 5 figures, submitted to AMIA Symposium 

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Learning concise representations for regression by evolving networks of trees


Oct 05, 2018
William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore

* 16 pages, 11 figures (including Appendix) 

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Where are we now? A large benchmark study of recent symbolic regression methods


Jun 07, 2018
Patryk Orzechowski, William La Cava, Jason H. Moore

* 8 pages, 4 figures. GECCO 2018 

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A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection


Apr 29, 2018
William La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore

* 30 pages, 8 figures. To appear in Evolutionary Computation Journal 

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Data-driven Advice for Applying Machine Learning to Bioinformatics Problems


Jan 07, 2018
Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore

* 12 pages, 5 figures, 4 tables. To be published in the proceedings of PSB 2018. Randal S. Olson and William La Cava contributed equally as co-first authors 

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A System for Accessible Artificial Intelligence


Aug 10, 2017
Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore

* 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 workshop 

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Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods


Aug 03, 2017
William La Cava, Jason H. Moore

* Genetic and Evolutionary Computation Conference (GECCO) 2017, Berlin, Germany 

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PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison


Mar 01, 2017
Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore

* 14 pages, 5 figures, submitted for review to JMLR 

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