15 citations · 33 across the 4 of their papers we have counts for
5 papers
PCE: Model-Agnostic Plausible Pareto-Optimal Counterfactual Explanations
Arthur Hendricks Mendes de Oliveira, Giovani Valdrighi, Marcos Medeiros Raimundo
The increasing use of machine learning algorithms in social applications has raised concerns about fairness and transparency, leading to the development of counterfactual explanati…
MFGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness
Jansen S. B. Pereira, Giovani Valdrighi, Marcos Medeiros Raimundo
In recent years, fairness in machine learning has emerged as a critical concern to ensure that developed and deployed predictive models do not have disadvantageous predictions for…
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales
Lucas E. Resck, Marcos M. Raimundo, Jorge Poco
Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model's reasoning, they may not…
An Extension of the Non-Inferior Set Estimation Algorithm for Many Objectives
Marcos M. Raimundo, Paulo A. V. Ferreira, Fernando J. Von Zuben
This work proposes a novel multi-objective optimization approach that globally finds a representative non-inferior set of solutions, also known as Pareto-optimal solutions, by auto…
An Extension of the Non-Inferior Set Estimation Algorithm for Many Objectives
Marcos M. Raimundo, Fernando J. Von Zuben
This work proposes a novel multi-objective optimization approach that globally finds a representative non-inferior set of solutions, also known as Pareto-optimal solutions, by auto…