144 citations · 367 across the 18 of their papers we have counts for
8 papers · 1 filter
Symbolic Regression is NP-hard
Marco Virgolin, Solon P. Pissis
Symbolic regression (SR) is the task of learning a model of data in the form of a mathematical expression. By their nature, SR models have the potential to be accurate and human-in…
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
Marco Virgolin, Peter A. N. Bosman
Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A…
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
Marco Virgolin, Eric Medvet, Tanja Alderliesten +1
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in th…
On genetic programming representations and fitness functions for interpretable dimensionality reduction
Thomas Uriot, Marco Virgolin, Tanja Alderliesten +1
Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not pro…
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten +1
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sortin…
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning
Marco Virgolin, Andrea De Lorenzo, Tanja Alderliesten +1
Emotion recognition in children can help the early identification of, and intervention on, psychological complications that arise in stressful situations such as cancer treatment.…
Conversational Agents: Theory and Applications
Mattias Wahde, Marco Virgolin
In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that general…
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations
Marco Virgolin, Saverio Fracaros
Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs…