Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction. - Alberto Bartoli; Andrea De Lorenzo; Eric Medvet; Fabiano Tarlao; Marco Virgolin (GECCO, 2015)

Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning. - Marco Virgolin; Tanja Alderliesten; Cees Witteveen; Peter A. N. Bosman (GECCO, 2017)

Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. - Marco Virgolin; Tanja Alderliesten; Arjan Bel; Cees Witteveen; Peter A. N. Bosman (GECCO, 2018)

Unveiling evolutionary algorithm representation with DU maps. - Eric Medvet; Marco Virgolin; Mauro Castelli; Peter A. N. Bosman; Ivo Gonçalves; Tea Tusar (Genet. Program. Evolvable Mach., 2018)

Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. - Marco Virgolin; Tanja Alderliesten; Peter A. N. Bosman (GECCO, 2019)

A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions. - Marco Virgolin; Tanja Alderliesten; Cees Witteveen; Peter A. N. Bosman (CoRR, 2019)

Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms. - Marco Virgolin; Ziyuan Wang; Tanja Alderliesten; Peter A. N. Bosman (CoRR, 2019)

Learning a Formula of Interpretability to Learn Interpretable Formulas. - Marco Virgolin; Andrea De Lorenzo; Eric Medvet; Francesca Randone (PPSN (2), 2020)

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. - Marco Virgolin; Ziyuan Wang; Brian V. Balgobind; Irma W. E. M. van Dijk; Jan Wiersma; Petra S. Kroon; Geert O. R. Janssens; Marcel van Herk; D. C. Hodgson; L. Zadravec Zaletel; C. R. N. Rasch; Arjan Bel; et al. (CoRR, 2020)

Simple Simultaneous Ensemble Learning in Genetic Programming. - Marco Virgolin (CoRR, 2020)

On explaining machine learning models by evolving crucial and compact features. - Marco Virgolin; Tanja Alderliesten; Peter A. N. Bosman (Swarm Evol. Comput., 2020)

Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming. - Marco Virgolin (2020)

The five Is: Key principles for interpretable and safe conversational AI. - Mattias Wahde; Marco Virgolin (CIIS, 2021)

Local Search is a Remarkably Strong Baseline for Neural Architecture Search. - Tom Den Ottelander; Arkadiy Dushatskiy; Marco Virgolin; Peter A. N. Bosman (EMO, 2021)

Genetic programming is naturally suited to evolve bagging ensembles. - Marco Virgolin (GECCO, 2021)

Model learning with personalized interpretability estimation (ML-PIE). - Marco Virgolin; Andrea De Lorenzo; Francesca Randone; Eric Medvet; Mattias Wahde (GECCO Companion, 2021)

A Mobile Interactive Robot for Social Distancing in Hospitals. - Marco Virgolin; Mauro Bellone; Krister Wolff; Mattias Wahde (IRC, 2021)

Contemporary Symbolic Regression Methods and their Relative Performance. - William G. La Cava; Patryk Orzechowski; Bogdan Burlacu; Fabrício Olivetti de França; Marco Virgolin; Ying Jin; Michael Kommenda; Jason H. Moore (NeurIPS Datasets and Benchmarks, 2021)

Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. - Marco Virgolin; Tanja Alderliesten; Cees Witteveen; Peter A. N. Bosman (Evol. Comput., 2021)

Evolvability degeneration in multi-objective genetic programming for symbolic regression. - Dazhuang Liu; Marco Virgolin; Tanja Alderliesten; Peter A. N. Bosman (GECCO, 2022)

On genetic programming representations and fitness functions for interpretable dimensionality reduction. - Thomas Uriot; Marco Virgolin; Tanja Alderliesten; Peter A. N. Bosman (GECCO, 2022)

Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. - Marco Virgolin; Peter A. N. Bosman (GECCO Companion, 2022)

On the Robustness of Counterfactual Explanations to Adverse Perturbations. - Marco Virgolin; Saverio Fracaros (CoRR, 2022)

Conversational Agents: Theory and Applications. - Mattias Wahde; Marco Virgolin (CoRR, 2022)

Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning. - Marco Virgolin; Andrea De Lorenzo; Tanja Alderliesten; Peter A. N. Bosman (CoRR, 2022)

Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning. - Marco Virgolin; Eric Medvet; Tanja Alderliesten; Peter A. N. Bosman (CoRR, 2022)

Symbolic Regression is NP-hard. - Marco Virgolin; Solon P. Pissis (Trans. Mach. Learn. Res., 2022)

Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression? - Joe Harrison; Marco Virgolin; Tanja Alderliesten; Peter A. N. Bosman (GECCO, 2023)

Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. - Pierre-Alexandre Kamienny; Guillaume Lample; Sylvain Lamprier; Marco Virgolin (ICML, 2023)

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (CoRR, 2023)

On the robustness of sparse counterfactual explanations to adverse perturbations. - Marco Virgolin; Saverio Fracaros (Artif. Intell., 2023)

DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI. - Mattias Wahde; Marco Virgolin (Int. J. Hum. Comput. Interact., 2023)

Parameterless Gene-Pool Optimal Mixing Evolutionary Algorithms. - Arkadiy Dushatskiy; Marco Virgolin; Anton Bouter; Dirk Thierens; Peter A. N. Bosman (Evol. Comput., 2024)

An interpretable method for automated classification of spoken transcripts and written text. - Mattias Wahde; Marco L. Della Vedova; Marco Virgolin; Minerva Suvanto (Evol. Intell., 2024)

An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming. - Giorgia Nadizar; Luigi Rovito; Andrea De Lorenzo; Eric Medvet; Marco Virgolin (ACM Trans. Evol. Learn. Optim., 2024)

Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression. - Luigi Rovito; Marco Virgolin (GECCO, 2025)

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (IEEE Trans. Evol. Comput., 2025)