15 citations · 20 across the 7 of their papers we have counts for
8 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…
Explanation-by-Example Based on Item Response Theory
Lucas F. F. Cardoso, José de S. Ribeiro, Vitor C. A. Santos +4
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have cha…
Data vs classifiers, who wins?
Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. Kawasaki Francês +2
The experiments covered by Machine Learning (ML) must consider two important aspects to assess the performance of a model: datasets and algorithms. Robust benchmarks are needed to…
Does Dataset Complexity Matters for Model Explainers?
José Ribeiro, Raíssa Silva, Lucas Cardoso +1
Strategies based on Explainable Artificial Intelligence - XAI have emerged in computing to promote a better understanding of predictions made by black box models. Most XAI measures…
Decoding machine learning benchmarks
Lucas F. F. Cardoso, Vitor C. A. Santos, Regiane S. K. Francês +2
Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best…
NASirt: AutoML based learning with instance-level complexity information
Habib Asseiss Neto, Ronnie C. O. Alves, Sergio V. A. Campos
Designing adequate and precise neural architectures is a challenging task, often done by highly specialized personnel. AutoML is a machine learning field that aims to generate good…