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02/22/2023
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Symbolic regression (SR) is the problem of learning a symbolic expressio...
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07/03/2022
Symbolic Regression is NP-hard
Symbolic regression (SR) is the task of learning a model of data in the ...
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04/26/2022
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
Currently, the genetic programming version of the gene-pool optimal mixi...
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04/05/2022
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
Interpretability can be critical for the safe and responsible use of mac...
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03/01/2022
On genetic programming representations and fitness functions for interpretable dimensionality reduction
Dimensionality reduction (DR) is an important technique for data explora...
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02/14/2022
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression
Genetic programming (GP) is one of the best approaches today to discover...
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02/10/2022
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning
Emotion recognition in children can help the early identification of, an...
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02/07/2022
Conversational Agents: Theory and Applications
In this chapter, we provide a review of conversational agents (CAs), dis...
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01/22/2022
On the Robustness of Counterfactual Explanations to Adverse Perturbations
Counterfactual explanations (CEs) are a powerful means for understanding...
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09/11/2021
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
When it comes to solving optimization problems with evolutionary algorit...
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08/31/2021
The five Is: Key principles for interpretable and safe conversational AI
In this position paper, we present five key principles, namely interpret...
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07/29/2021
Contemporary Symbolic Regression Methods and their Relative Performance
Many promising approaches to symbolic regression have been presented in ...
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04/13/2021
Model Learning with Personalized Interpretability Estimation (ML-PIE)
High-stakes applications require AI-generated models to be interpretable...
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09/13/2020
Simple Simultaneous Ensemble Learning in Genetic Programming
Learning ensembles by bagging can substantially improve the generalizati...
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04/23/2020
Learning a Formula of Interpretability to Learn Interpretable Formulas
Many risk-sensitive applications require Machine Learning (ML) models to...
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09/09/2019
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
Machine Learning (ML) is proving extremely beneficial in many healthcare...
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07/04/2019
On Explaining Machine Learning Models by Evolving Crucial and Compact Features
Feature construction can substantially improve the accuracy of Machine L...
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04/03/2019
A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-b...
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04/03/2019