4 papers
LLM-Driven Intrinsic Motivation for Sparse Reward Reinforcement Learning
André Quadros, Cassio Silva, Ronnie Alves
This paper explores the combination of two intrinsic motivation strategies to improve the efficiency of reinforcement learning (RL) agents in environments with extreme sparse rewar…
Beyond Random Sampling: Instance Quality-Based Data Partitioning via Item Response Theory
Lucas Cardoso, Vitor Santos, José Ribeiro Filho +3
Robust validation of Machine Learning (ML) models is essential, but traditional data partitioning approaches often ignore the intrinsic quality of each instance. This study propose…
Enhancing Classifier Evaluation: A Fairer Benchmarking Strategy Based on Ability and Robustness
Lucas Cardoso, Vitor Santos, José Ribeiro +3
Benchmarking is a fundamental practice in machine learning (ML) for comparing the performance of classification algorithms. However, traditional evaluation methods often overlook a…
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
José Ribeiro, Lucas Cardoso, RaÃssa Silva +3
In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method…