90 citations · 124 across the 10 of their papers we have counts for
3 papers · 1 filter
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
Sérgio Jesus, José Pombal, Duarte Alves +5
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a…
FairGBM: Gradient Boosting with Fairness Constraints
André F Cruz, Catarina Belém, Sérgio Jesus +3
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to thei…
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus +6
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robu…