Marco Virgolin

51 papers A* 1A 9C 1Journal 33Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure.
CoRR
Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin, Giovanni Cinà
2025 A conf
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression.
GECCO
Luigi Rovito, Marco Virgolin
2025 J jnl
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression.
CoRR
Luigi Rovito, Marco Virgolin
2025 J jnl
SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation.
IEEE Trans. Evol. Comput.
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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2024 J jnl
An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming.
ACM Trans. Evol. Learn. Optim.
Giorgia Nadizar, Luigi Rovito, Andrea De Lorenzo, Eric Medvet, Marco Virgolin
2024 J jnl
An interpretable method for automated classification of spoken transcripts and written text.
Evol. Intell.
Mattias Wahde, Marco L. Della Vedova, Marco Virgolin, Minerva Suvanto
2024 J jnl
Parameterless Gene-Pool Optimal Mixing Evolutionary Algorithms.
Evol. Comput.
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A. N. Bosman
2023 J jnl
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI.
Int. J. Hum. Comput. Interact.
Mattias Wahde, Marco Virgolin
2023 A* conf
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search.
ICML
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin
2023 J jnl
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search.
CoRR
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin
2023 J jnl
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.
CoRR
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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2023 A conf
Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?
GECCO
Joe Harrison, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2023 J jnl
On the robustness of sparse counterfactual explanations to adverse perturbations.
Artif. Intell.
Marco Virgolin, Saverio Fracaros
2022 J jnl
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning.
CoRR
Marco Virgolin, Andrea De Lorenzo, Tanja Alderliesten, Peter A. N. Bosman
2022 J jnl
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression.
CoRR
Marco Virgolin, Peter A. N. Bosman
2022 — conf
Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression.
GECCO Companion
Marco Virgolin, Peter A. N. Bosman
2022 J jnl
Conversational Agents: Theory and Applications.
CoRR
Mattias Wahde, Marco Virgolin
2022 J jnl
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression.
CoRR
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2022 A conf
Evolvability degeneration in multi-objective genetic programming for symbolic regression.
GECCO
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2022 J jnl
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning.
CoRR
Marco Virgolin, Eric Medvet, Tanja Alderliesten, Peter A. N. Bosman
2022 A conf
On genetic programming representations and fitness functions for interpretable dimensionality reduction.
GECCO
Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2022 J jnl
On genetic programming representations and fitness functions for interpretable dimensionality reduction.
CoRR
Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2022 J jnl
On the Robustness of Counterfactual Explanations to Adverse Perturbations.
CoRR
Marco Virgolin, Saverio Fracaros
2022 J jnl
Symbolic Regression is NP-hard.
CoRR
Marco Virgolin, Solon P. Pissis
2022 J jnl
Symbolic Regression is NP-hard.
Trans. Mach. Learn. Res.
Marco Virgolin, Solon P. Pissis
2021 — conf
A Mobile Interactive Robot for Social Distancing in Hospitals.
IRC
Marco Virgolin, Mauro Bellone, Krister Wolff, Mattias Wahde
2021 — conf
Contemporary Symbolic Regression Methods and their Relative Performance.
NeurIPS Datasets and Benchmarks
William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
2021 J jnl
Contemporary Symbolic Regression Methods and their Relative Performance.
CoRR
William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
2021 A conf
Genetic programming is naturally suited to evolve bagging ensembles.
GECCO
Marco Virgolin
2021 J jnl
Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions.
Evol. Comput.
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
2021 C conf
Local Search is a Remarkably Strong Baseline for Neural Architecture Search.
EMO
Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman
2021 J jnl
Model Learning with Personalized Interpretability Estimation (ML-PIE).
CoRR
Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde
2021 — conf
Model learning with personalized interpretability estimation (ML-PIE).
GECCO Companion
Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde
2021 J jnl
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms.
CoRR
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A. N. Bosman
2021 — conf
The five Is: Key principles for interpretable and safe conversational AI.
CIIS
Mattias Wahde, Marco Virgolin
2021 J jnl
The five Is: Key principles for interpretable and safe conversational AI.
CoRR
Mattias Wahde, Marco Virgolin
2020 — —
Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming.
Marco Virgolin
2020 — conf
Learning a Formula of Interpretability to Learn Interpretable Formulas.
PPSN (2)
Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone
2020 J jnl
Learning a Formula of Interpretability to Learn Interpretable Formulas.
CoRR
Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone
2020 J jnl
Local Search is a Remarkably Strong Baseline for Neural Architecture Search.
CoRR
Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman
2020 J jnl
On explaining machine learning models by evolving crucial and compact features.
Swarm Evol. Comput.
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2020 J jnl
Simple Simultaneous Ensemble Learning in Genetic Programming.
CoRR
Marco Virgolin
2020 J jnl
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy.
CoRR
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, Peter A. N. Bosman, Tanja Alderliesten
2019 J jnl
A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions.
CoRR
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
2019 A conf
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression.
GECCO
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2019 J jnl
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms.
CoRR
Marco Virgolin, Ziyuan Wang, Tanja Alderliesten, Peter A. N. Bosman
2019 J jnl
On Explaining Machine Learning Models by Evolving Crucial and Compact Features.
CoRR
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
2018 A conf
Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors.
GECCO
Marco Virgolin, Tanja Alderliesten, Arjan Bel, Cees Witteveen, Peter A. N. Bosman
2018 J jnl
Unveiling evolutionary algorithm representation with DU maps.
Genet. Program. Evolvable Mach.
Eric Medvet, Marco Virgolin, Mauro Castelli, Peter A. N. Bosman, Ivo Gonçalves, Tea Tusar
2017 A conf
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning.
GECCO
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
2015 A conf
Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction.
GECCO
Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, Fabiano Tarlao, Marco Virgolin