144 citations · 367 across the 18 of their papers we have counts for
4 papers · 1 filter
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter +2
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencie…
The five Is: Key principles for interpretable and safe conversational AI
Mattias Wahde, Marco Virgolin
In this position paper, we present five key principles, namely interpretability, inherent capability to explain, independent data, interactive learning, and inquisitiveness, for th…
Contemporary Symbolic Regression Methods and their Relative Performance
William La Cava, Patryk Orzechowski, Bogdan Burlacu +5
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent…
Model Learning with Personalized Interpretability Estimation (ML-PIE)
Marco Virgolin, Andrea De Lorenzo, Francesca Randone +2
High-stakes applications require AI-generated models to be interpretable. Current algorithms for the synthesis of potentially interpretable models rely on objectives or regularizat…