1 citations · 1 across the 4 of their papers we have counts for
4 papers
Model-to-Data Distillation for Graph Neural Networks
Debolina Halder Lina, Arlei Silva
Graph neural networks (GNNs) increasingly rely on sophisticated architectures and training procedures to achieve desirable properties such as high predictive performance, fairness,…
Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity
João Mattos, Debolina Halder Lina, Arlei Silva
Link prediction is a fundamental task in graph machine learning with applications, ranging from social recommendation to knowledge graph completion. Fairness in this setting is cri…
Fair Graph Machine Learning under Adversarial Missingness Processes
Debolina Halder Lina, Arlei Silva
Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing w…
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates
Camille Olivia Little, Debolina Halder Lina, Genevera I. Allen
Across various sectors such as healthcare, criminal justice, national security, finance, and technology, large-scale machine learning (ML) and artificial intelligence (AI) systems…