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Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms

Sep 11, 2021
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A. N. Bosman

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The five Is: Key principles for interpretable and safe conversational AI

Aug 31, 2021
Mattias Wahde, Marco Virgolin

* 6 pages, one figure 

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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. 

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Model Learning with Personalized Interpretability Estimation (ML-PIE)

Apr 27, 2021
Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde

* fix typos 

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Simple Simultaneous Ensemble Learning in Genetic Programming

Oct 01, 2020
Marco Virgolin

* Included results for SS+BE; Rerun experiments with z-scoring only based on training and updated tables and figures (not significantly different but more precise); Fixed some textual errors, included suggestions, updated acknowledgments 

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Learning a Formula of Interpretability to Learn Interpretable Formulas

May 28, 2020
Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone

* 16 pages, 4 figures Accepted at PPSN2020 

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Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms

Sep 09, 2019
Marco Virgolin, Ziyuan Wang, Tanja Alderliesten, Peter A. N. Bosman

* Currently submitted to SPIE Medical Imaging journal 

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On Explaining Machine Learning Models by Evolving Crucial and Compact Features

Jul 04, 2019
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman

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A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions

Apr 05, 2019
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman

* Currently submitted for peer review to IEEE Transactions on Evolutionary Computation 

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