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Marco Virgolin
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2020 – today
- 2024
- [j8]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): 609-621 (2024) - [j7]Giorgia Nadizar
, Luigi Rovito
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): 5:1-5:30 (2024) - 2023
- [j6]Marco Virgolin
On the robustness of sparse counterfactual explanations to adverse perturbations. Artif. Intell. 316: 103840 (2023) - [j5]Mattias Wahde, Marco Virgolin:
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI. Int. J. Hum. Comput. Interact. 39(9): 1856-1873 (2023) - [c16]Joe Harrison
Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression? GECCO 2023: 1127-1136 - [c15]Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin:
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. ICML 2023: 15655-15668 - [i21]Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin:
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. CoRR abs/2302.11223 (2023) - [i20]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - 2022
- [j4]Marco Virgolin, Solon P. Pissis:
Symbolic Regression is NP-hard. Trans. Mach. Learn. Res. 2022 (2022) - [c14]Thomas Uriot, Marco Virgolin, Tanja Alderliesten
On genetic programming representations and fitness functions for interpretable dimensionality reduction. GECCO 2022: 458-466 - [c13]Dazhuang Liu, Marco Virgolin, Tanja Alderliesten
Evolvability degeneration in multi-objective genetic programming for symbolic regression. GECCO 2022: 973-981 - [c12]Marco Virgolin, Peter A. N. Bosman:
Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. GECCO Companion 2022: 2289-2297 - [i19]Marco Virgolin, Saverio Fracaros:
On the Robustness of Counterfactual Explanations to Adverse Perturbations. CoRR abs/2201.09051 (2022) - [i18]Mattias Wahde, Marco Virgolin:
Conversational Agents: Theory and Applications. CoRR abs/2202.03164 (2022) - [i17]Marco Virgolin, Andrea De Lorenzo, Tanja Alderliesten, Peter A. N. Bosman:
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning. CoRR abs/2202.05187 (2022) - [i16]Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression. CoRR abs/2202.06983 (2022) - [i15]Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
On genetic programming representations and fitness functions for interpretable dimensionality reduction. CoRR abs/2203.00528 (2022) - [i14]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 abs/2204.02046 (2022) - [i13]Marco Virgolin, Peter A. N. Bosman:
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression. CoRR abs/2204.12159 (2022) - [i12]Marco Virgolin, Solon P. Pissis:
Symbolic Regression is NP-hard. CoRR abs/2207.01018 (2022) - 2021
- [j3]Marco Virgolin, Tanja Alderliesten
Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. Evol. Comput. 29(2): 211-237 (2021) - [c11]Mattias Wahde, Marco Virgolin:
The five Is: Key principles for interpretable and safe conversational AI. CIIS 2021: 50-54 - [c10]Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman:
Local Search is a Remarkably Strong Baseline for Neural Architecture Search. EMO 2021: 465-479 - [c9]Marco Virgolin:
Genetic programming is naturally suited to evolve bagging ensembles. GECCO 2021: 830-839 - [c8]Marco Virgolin, Andrea De Lorenzo, Francesca Randone
Model learning with personalized interpretability estimation (ML-PIE). GECCO Companion 2021: 1355-1364 - [c7]Marco Virgolin, Mauro Bellone
A Mobile Interactive Robot for Social Distancing in Hospitals. IRC 2021: 87-91 - [c6]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 - [i11]Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde:
Model Learning with Personalized Interpretability Estimation (ML-PIE). CoRR abs/2104.06060 (2021) - [i10]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 abs/2107.14351 (2021) - [i9]Mattias Wahde, Marco Virgolin:
The five Is: Key principles for interpretable and safe conversational AI. CoRR abs/2108.13766 (2021) - [i8]Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A. N. Bosman:
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms. CoRR abs/2109.05259 (2021) - 2020
- [b1]Marco Virgolin:
Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming. Delft University of Technology, Netherlands, 2020 - [j2]Marco Virgolin, Tanja Alderliesten
On explaining machine learning models by evolving crucial and compact features. Swarm Evol. Comput. 53: 100640 (2020) - [c5]Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone
Learning a Formula of Interpretability to Learn Interpretable Formulas. PPSN (2) 2020: 79-93 - [i7]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 abs/2002.07161 (2020) - [i6]Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman:
Local Search is a Remarkably Strong Baseline for Neural Architecture Search. CoRR abs/2004.08996 (2020) - [i5]Marco Virgolin, Andrea De Lorenzo, Eric Medvet, Francesca Randone:
Learning a Formula of Interpretability to Learn Interpretable Formulas. CoRR abs/2004.11170 (2020) - [i4]Marco Virgolin:
Simple Simultaneous Ensemble Learning in Genetic Programming. CoRR abs/2009.06037 (2020)
2010 – 2019
- 2019
- [c4]Marco Virgolin, Tanja Alderliesten
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. GECCO 2019: 1084-1092 - [i3]Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman:
A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions. CoRR abs/1904.02050 (2019) - [i2]Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
On Explaining Machine Learning Models by Evolving Crucial and Compact Features. CoRR abs/1907.02260 (2019) - [i1]Marco Virgolin, Ziyuan Wang, Tanja Alderliesten, Peter A. N. Bosman:
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms. CoRR abs/1909.03723 (2019) - 2018
- [j1]Eric Medvet
Unveiling evolutionary algorithm representation with DU maps. Genet. Program. Evolvable Mach. 19(3): 351-389 (2018) - [c3]Marco Virgolin, Tanja Alderliesten
Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. GECCO 2018: 1395-1402 - 2017
- [c2]Marco Virgolin, Tanja Alderliesten
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning. GECCO 2017: 1041-1048 - 2015
- [c1]Alberto Bartoli
Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction. GECCO 2015: 1183-1190
Coauthor Index
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last updated on 2024-05-08 21:48 CEST by the dblp team
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