| Contemporary symbolic regression methods and their relative performance W La Cava, P Orzechowski, B Burlacu, FO de França, M Virgolin, Y Jin, ... NeurIPS - Datasets & Benchmark Track, 2021 | 259 | 2021 |
| Improving model-based genetic programming for symbolic regression of small expressions M Virgolin, T Alderliesten, C Witteveen, PAN Bosman Evolutionary computation 29 (2), 211-237, 2021 | 116 | 2021 |
| Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression M Virgolin, T Alderliesten, PAN Bosman Proceedings of the genetic and evolutionary computation conference, 1084-1092, 2019 | 67 | 2019 |
| Symbolic Regression is NP-hard M Virgolin, SP Pissis Transactions on Machine Learning Research, 2022 | 53 | 2022 |
| Local search is a remarkably strong baseline for neural architecture search T Den Ottelander, A Dushatskiy, M Virgolin, PAN Bosman 🏆 Evolutionary Multi-Criterion Optimization: 11th International Conference …, 2021 | 51 | 2021 |
| Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning M Virgolin, T Alderliesten, C Witteveen, PAN Bosman Proceedings of the Genetic and Evolutionary Computation Conference, 1041-1048, 2017 | 50 | 2017 |
| Learning a formula of interpretability to learn interpretable formulas M Virgolin, A De Lorenzo, E Medvet, F Randone Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020 | 43 | 2020 |
| Conversational agents: Theory and applications M Wahde, M Virgolin HANDBOOK ON COMPUTER LEARNING AND INTELLIGENCE: Volume 2: Deep Learning …, 2022 | 40 | 2022 |
| On explaining machine learning models by evolving crucial and compact features M Virgolin, T Alderliesten, PAN Bosman Swarm and Evolutionary Computation 53, 100640, 2020 | 37 | 2020 |
| Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors M Virgolin, T Alderliesten, A Bel, C Witteveen, PAN Bosman Proceedings of the Genetic and evolutionary computation conference, 1395-1402, 2018 | 37 | 2018 |
| On the robustness of sparse counterfactual explanations to adverse perturbations M Virgolin, S Fracaros Artificial Intelligence 316, 103840, 2023 | 34* | 2023 |
| Model learning with personalized interpretability estimation (ML-PIE) M Virgolin, A De Lorenzo, F Randone, E Medvet, M Wahde Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 28 | 2021 |
| Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search PA Kamienny, G Lample, S Lamprier, M Virgolin International Conference on Machine Learning, 2023 | 19 | 2023 |
| Genetic programming is naturally suited to evolve bagging ensembles M Virgolin Proceedings of the Genetic and Evolutionary Computation Conference, 830-839, 2021 | 19 | 2021 |
| Unveiling evolutionary algorithm representation with DU maps E Medvet, M Virgolin, M Castelli, PAN Bosman, I Gonçalves, T Tušar Genetic Programming and Evolvable Machines 19, 351-389, 2018 | 15 | 2018 |
| Evolvability degeneration in multi-objective genetic programming for symbolic regression D Liu, M Virgolin, T Alderliesten, PAN Bosman 🏆 Proceedings of the Genetic and Evolutionary Computation Conference, 973-981, 2022 | 14 | 2022 |
| Daisy: An implementation of five core principles for transparent and accountable conversational AI M Wahde, M Virgolin International Journal of Human–Computer Interaction 39 (9), 1856-1873, 2023 | 13 | 2023 |
| On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors M Virgolin, IWEM van Dijk, J Wiersma, CM Ronckers, C Witteveen, A Bel, ... Medical physics 45 (4), 1504-1517, 2018 | 13 | 2018 |
| On genetic programming representations and fitness functions for interpretable dimensionality reduction T Uriot, M Virgolin, T Alderliesten, PAN Bosman Proceedings of the Genetic and Evolutionary Computation Conference, 458-466, 2022 | 12 | 2022 |
| Interpretable symbolic regression for data science: analysis of the 2022 competition FO de França, M Virgolin, M Kommenda, MS Majumder, M Cranmer, ... arXiv preprint arXiv:2304.01117, 2023 | 11 | 2023 |
