Marco Virgolin

According to our database, Marco Virgolin authored at least 35 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., March, 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

2023

DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI.

Mattias Wahde, Marco Virgolin

Int. J. Hum. Comput. Interact., May, 2023

On the robustness of sparse counterfactual explanations to adverse perturbations.

Marco Virgolin, Saverio Fracaros

Artif. Intell., March, 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, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava

CoRR, 2023

Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search.

Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin

Proceedings of the International Conference on Machine Learning, 2023

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

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

2022

Symbolic Regression is NP-hard.

Marco Virgolin, Solon P. Pissis

Trans. Mach. Learn. Res., 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

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

Conversational Agents: Theory and Applications.

Mattias Wahde, Marco Virgolin

CoRR, 2022

On the Robustness of Counterfactual Explanations to Adverse Perturbations.

Marco Virgolin, Saverio Fracaros

CoRR, 2022

Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression.

Marco Virgolin, Peter A. N. Bosman

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

On genetic programming representations and fitness functions for interpretable dimensionality reduction.

Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Evolvability degeneration in multi-objective genetic programming for symbolic regression.

Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

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

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms.

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

CoRR, 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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

A Mobile Interactive Robot for Social Distancing in Hospitals.

Marco Virgolin, Mauro Bellone, Krister Wolff, Mattias Wahde

Proceedings of the Fifth IEEE International Conference on Robotic Computing, 2021

Model learning with personalized interpretability estimation (ML-PIE).

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

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Genetic programming is naturally suited to evolve bagging ensembles.

Marco Virgolin

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Local Search is a Remarkably Strong Baseline for Neural Architecture Search.

Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman

Proceedings of the Evolutionary Multi-Criterion Optimization, 2021

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

Mattias Wahde, Marco Virgolin

Proceedings of the CIIS 2021: The 4th International Conference on Computational Intelligence and Intelligent Systems, Tokyo, Japan, November 20, 2021

2020

Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming.

Marco Virgolin

PhD thesis, 2020

On explaining machine learning models by evolving crucial and compact features.

Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman

Swarm Evol. Comput., 2020

Simple Simultaneous Ensemble Learning in Genetic Programming.

Marco Virgolin

CoRR, 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, Peter A. N. Bosman, Tanja Alderliesten

CoRR, 2020

Learning a Formula of Interpretability to Learn Interpretable Formulas.

Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

2019

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

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

CoRR, 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

Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression.

Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

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

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

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

2017

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

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2015

Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction.

Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, Fabiano Tarlao, Marco Virgolin

Proceedings of the Genetic and Evolutionary Computation Conference, 2015