Marco Virgolin

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 45

Top Topics

Automatic Construction

Symbolic Regression

Genetic Programming

Conversational Agents

Top Venues

GECCO Companion

ACM Trans. Evol. Learn. Optim.

Publications

Giorgia Nadizar, Luigi Rovito, Andrea De Lorenzo, Eric Medvet, Marco Virgolin
An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming. ACM Trans. Evol. Learn. Optim. 4 (1) (2024)
Mattias Wahde, Marco L. Della Vedova, Marco Virgolin, Minerva Suvanto
An interpretable method for automated classification of spoken transcripts and written text. Evol. Intell. 17 (1) (2024)
Marco Virgolin, Saverio Fracaros
On the robustness of sparse counterfactual explanations to adverse perturbations. Artif. Intell. 316 (2023)
Joe Harrison, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression? GECCO (2023)
Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, M. S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, B. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, G. Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR (2023)
Mattias Wahde, Marco Virgolin
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI. Int. J. Hum. Comput. Interact. 39 (9) (2023)
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. CoRR (2023)
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. ICML (2023)
Marco Virgolin, Peter A. N. Bosman
Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. GECCO Companion (2022)
Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
On genetic programming representations and fitness functions for interpretable dimensionality reduction. CoRR (2022)
Mattias Wahde, Marco Virgolin
Conversational Agents: Theory and Applications. CoRR (2022)
Marco Virgolin, Andrea De Lorenzo, Tanja Alderliesten, Peter A. N. Bosman
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning. CoRR (2022)
Marco Virgolin, Solon P. Pissis
Symbolic Regression is NP-hard. Trans. Mach. Learn. Res. 2022 (2022)
Marco Virgolin, Saverio Fracaros
On the Robustness of Counterfactual Explanations to Adverse Perturbations. CoRR (2022)
Marco Virgolin, Eric Medvet, Tanja Alderliesten, Peter A. N. Bosman
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning. CoRR (2022)
Marco Virgolin, Solon P. Pissis
Symbolic Regression is NP-hard. CoRR (2022)
Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
On genetic programming representations and fitness functions for interpretable dimensionality reduction. GECCO (2022)
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression. CoRR (2022)
Marco Virgolin, Peter A. N. Bosman
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression. CoRR (2022)
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Evolvability degeneration in multi-objective genetic programming for symbolic regression. GECCO (2022)
Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman
Local Search is a Remarkably Strong Baseline for Neural Architecture Search. EMO (2021)
Mattias Wahde, Marco Virgolin
The five Is: Key principles for interpretable and safe conversational AI. CIIS (2021)
William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
Contemporary Symbolic Regression Methods and their Relative Performance. NeurIPS Datasets and Benchmarks (2021)
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A. N. Bosman
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms. CoRR (2021)
Mattias Wahde, Marco Virgolin
The five Is: Key principles for interpretable and safe conversational AI. CoRR (2021)
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. Evol. Comput. 29 (2) (2021)
William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
Contemporary Symbolic Regression Methods and their Relative Performance. CoRR (2021)
Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde
Model Learning with Personalized Interpretability Estimation (ML-PIE). CoRR (2021)
Marco Virgolin
Genetic programming is naturally suited to evolve bagging ensembles. GECCO (2021)
Marco Virgolin, Mauro Bellone, Krister Wolff, Mattias Wahde
A Mobile Interactive Robot for Social Distancing in Hospitals. IRC (2021)
Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde
Model learning with personalized interpretability estimation (ML-PIE). GECCO Companion (2021)
Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone
Learning a Formula of Interpretability to Learn Interpretable Formulas. PPSN (2) (2020)
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
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. CoRR (2020)
Marco Virgolin
Simple Simultaneous Ensemble Learning in Genetic Programming. CoRR (2020)
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
On explaining machine learning models by evolving crucial and compact features. Swarm Evol. Comput. 53 (2020)
Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman
Local Search is a Remarkably Strong Baseline for Neural Architecture Search. CoRR (2020)
Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone
Learning a Formula of Interpretability to Learn Interpretable Formulas. CoRR (2020)
Marco Virgolin, Ziyuan Wang, Tanja Alderliesten, Peter A. N. Bosman
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms. CoRR (2019)
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions. CoRR (2019)
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. GECCO (2019)
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
On Explaining Machine Learning Models by Evolving Crucial and Compact Features. CoRR (2019)
Marco Virgolin, Tanja Alderliesten, Arjan Bel, Cees Witteveen, Peter A. N. Bosman
Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. GECCO (2018)
Eric Medvet, Marco Virgolin, Mauro Castelli, Peter A. N. Bosman, Ivo Gonçalves, Tea Tusar
Unveiling evolutionary algorithm representation with DU maps. Genet. Program. Evolvable Mach. 19 (3) (2018)
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning. GECCO (2017)
Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, Fabiano Tarlao, Marco Virgolin
Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction. GECCO (2015)