Evolutionary learning of interpretable decision trees

14 December 2020

Papers citing "Evolutionary learning of interpretable decision trees"

14 / 14 papers shown

Title
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management Stefano Genetti Alberto Longobardi Giovanni Iacca 51 0 0 16 Apr 2025
SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks Mátyás Vincze Laura Ferrarotti Leonardo Lucio Custode Bruno Lepri Giovanni Iacca MoE OffRL 66 1 0 17 Dec 2024
Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems Ryan Zhou Jaume Bacardit Alexander Brownlee Stefano Cagnoni Martin Fyvie Giovanni Iacca John Mccall N. V. Stein David Walker Ting-Kuei Hu 39 0 0 12 Jun 2024
Evo* 2023 -- Late-Breaking Abstracts Volume A. M. Mora Christopher J. Tralie 23 0 0 20 Mar 2024
Social Interpretable Reinforcement Learning Leonardo Lucio Custode Giovanni Iacca OffRL 37 2 0 27 Jan 2024
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey R. Alizadehsani Solomon Sunday Oyelere Sadiq Hussain Rene Ripardo Calixto V. H. C. de Albuquerque M. Roshanzamir Mohamed Rahouti Senthil Kumar Jagatheesaperumal 37 17 0 21 Sep 2023
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm Julian Ruddick L. R. Camargo M. A. Putratama M. Messagie Thierry Coosemans 10 2 0 17 Apr 2023
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges Yunpeng Qing Shunyu Liu Jie Song Huiqiong Wang Mingli Song XAI 25 1 0 12 Nov 2022
Quality Diversity Evolutionary Learning of Decision Trees Andrea Ferigo Leonardo Lucio Custode Giovanni Iacca 19 12 0 17 Aug 2022
Interpretable AI for policy-making in pandemics Leonardo Lucio Custode Giovanni Iacca 17 9 0 08 Apr 2022
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs Leonardo Lucio Custode Giovanni Iacca 13 13 0 10 Feb 2022
Model Learning with Personalized Interpretability Estimation (ML-PIE) M. Virgolin A. D. Lorenzo Francesca Randone Eric Medvet M. Wahde 16 29 0 13 Apr 2021
Model Interpretability through the Lens of Computational Complexity Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 116 94 0 23 Oct 2020
Reinforcement Learning for Optimization of COVID-19 Mitigation policies Varun Kompella Roberto Capobianco Stacy Jong Jonathan Browne S. Fox L. Meyers Peter R. Wurman Peter Stone 64 46 0 20 Oct 2020