Marco Virgolin

Full Name

M. Virgolin

M.Virgolin@cwi.nl

Phone

+31 20 592 4017

Room

M277b

Department(s)

Evolutionary Intelligence

Function(s)

Scientific Staff Member

Homepage

https://marcovirgolin.github.io/

Biography

Marco Virgolin is a junior researcher (tenure track) at CWI. He works on eXplainable AI (XAI), most notably by means of Symbolic Regression to obtain interpretable models. He is also interested in medical applications of machine learning, conversational AI, and human-machine interaction. Before, Marco was a postdoc with Mattias Wahde at Chalmers University of Technology, a PhD student with Peter A. N. Bosman and Tanja Alderliesten at Centrum Wiskunde & Informatica, and a master student with Alberto Bartoli and Eric Medvet at the Machine Learning Lab of the University of Trieste.

Kamienny, P.-A, Lample, G, Lamprier, S, & Virgolin, M. (2023). Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. In Proceedings of the International Conference on Machine Learning (pp. 15655–15668).
Harrison, J, Virgolin, M, Alderliesten, T, & Bosman, P.A.N. (2023). Mini-Batching, Gradient-Clipping, first-versus second-order: What works in Gradient-Based coefficient optimisation for Symbolic Regression'. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1127–1136). doi:10.1145/3583131.3590368
Wahde, M, Della Vedova, M.L, Virgolin, M, & Suvanto, M. (2023). An interpretable method for automated classification of spoken transcripts and written text. Evolutionary Intelligence. doi:10.1007/s12065-023-00851-1
Wahde, M, Della Vedova, M.L, Virgolin, M, & Suvanto, M. (2023). Data set for (binary) text classification, involving spoken utterances and written text. doi:10.5281/zenodo.7694423
Virgolin, M, & Fracaros, S. (2023). On the robustness of sparse counterfactual explanations to adverse perturbations. Artificial Intelligence, 316, 103840.1–103840.30. doi:10.1016/j.artint.2022.103840
Wang, Z, Virgolin, M, Balgobind, B.V, Dijk, van, I.W.E.M, Smith, S.A, Howell, R.M, … Alderliesten, T. (2022). Validation and comparison of radiograph-based organ dose reconstruction approaches for Wilms tumor radiation treatment plans. Advances in Radiation Oncology, 7(6), 101015.1–101015.12. doi:10.1016/j.adro.2022.101015
Virgolin, M, & Pissis, S. (2022). Symbolic regression is NP-hard. Transactions on Machine Learning Research, 10, 1–11.
Virgolin, M, & Bosman, P.A.N. (2022). Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 2289–2297). doi:10.1145/3520304.3534036
Uriot, T, Virgolin, M, Alderliesten, T, & Bosman, P.A.N. (2022). On genetic programming representations and fitness functions for interpretable dimensionality reduction. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 458–466). doi:10.1145/3512290.3528849
Liu, D, Virgolin, M, Alderliesten, T, & Bosman, P.A.N. (2022). Evolvability degeneration in multi-objective genetic programming for symbolic regression. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 973–981). doi:10.1145/3512290.3528787

SIGEVO Dissertation Award 2021 (2021)
Silver HUMIES award (2021)
Best Paper award at the International Conference on EMO (2021)

Marco Virgolin

Biography

Publications

Awards

Marco Virgolin

Biography

Publications

Awards

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