Folgen
Marco Virgolin
Marco Virgolin
InSilicoTrials
Bestätigte E-Mail-Adresse bei insilicotrials.com
Titel
Zitiert von
Zitiert von
Jahr
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
2062021
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
972021
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
572019
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
492017
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
482021
Symbolic Regression is NP-hard
M Virgolin, SP Pissis
Transactions on Machine Learning Research, 2022
412022
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
382020
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
352018
On explaining machine learning models by evolving crucial and compact features
M Virgolin, T Alderliesten, PAN Bosman
Swarm and Evolutionary Computation 53, 100640, 2020
342020
Conversational agents: Theory and applications
M Wahde, M Virgolin
HANDBOOK ON COMPUTER LEARNING AND INTELLIGENCE: Volume 2: Deep Learning …, 2022
322022
On the robustness of sparse counterfactual explanations to adverse perturbations
M Virgolin, S Fracaros
Artificial Intelligence 316, 103840, 2023
31*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
262021
Genetic programming is naturally suited to evolve bagging ensembles
M Virgolin
Proceedings of the Genetic and Evolutionary Computation Conference, 830-839, 2021
162021
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
142018
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
132023
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
132018
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
122022
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
PA Kamienny, G Lample, S Lamprier, M Virgolin
International Conference on Machine Learning, 2023
112023
Parameterless gene-pool optimal mixing evolutionary algorithms
A Dushatskiy, M Virgolin, A Bouter, D Thierens, PAN Bosman
Evolutionary Computation, 1-28, 2023
92023
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
92022
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20