5 papers
Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings
Inês Oliveira e Silva, Sérgio Jesus, Iker Perez +4
Shapley values are a cornerstone of explainable AI, yet their proliferation into competing formulations has created a fragmented landscape with little consensus on practical deploy…
Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
Abolfazl Mohammadi-Seif, Carlos Soares, Rita P. Ribeiro +1
Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predict…
CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression
António Pedro Pinheiro, Rita P. Ribeiro
Handling imbalanced target distributions in regression poses a persistent challenge, as the underrepresentation of relevant target values can significantly hinder model performance…
Histogram Approaches for Imbalanced Data Streams Regression
Ehsan Aminian, Rita P. Ribeiro, Joao Gama
Imbalanced domains pose a significant challenge in real-world predictive analytics, particularly in the context of regression. While existing research has primarily focused on batc…
Aequitas Flow: Streamlining Fair ML Experimentation
Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva +5
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps tha…