1 citations · 2 across the 6 of their papers we have counts for
6 papers
Fair Feature Importance Scores via Feature Occlusion and Permutation
Camille Little, Madeline Navarro, Santiago Segarra +1
As machine learning models increasingly impact society, their opaque nature poses challenges to trust and accountability, particularly in fairness contexts. Understanding how indiv…
Top- Feature Importance Ranking
Yuxi Chen, Tiffany Tang, Genevera Allen
Accurate ranking of important features is a fundamental challenge in interpretable machine learning with critical applications in scientific discovery and decision-making. Unlike f…
iLOCO: Distribution-Free Inference for Feature Interactions
Camille Little, Lili Zheng, Genevera Allen
Feature importance measures are widely studied and are essential for understanding model behavior, guiding feature selection, and enhancing interpretability. However, many machine…
Fair MP-BOOST: Fair and Interpretable Minipatch Boosting
Camille Olivia Little, Genevera I. Allen
Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to…
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…
Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates
Tongtong Huang, Yan Chu, Shayan Shams +7
Objective: We study the influence of local reopening policies on the composition of the infectious population and their impact on future hospitalization and mortality rates. Materi…