12 citations · 16 across the 4 of their papers we have counts for
4 papers · 1 filter
Are machine learning interpretations reliable? A stability study on global interpretations
Luqin Gan, Tarek M. Zikry, Genevera I. Allen
As machine learning systems are increasingly used in high-stakes domains, there is a growing emphasis placed on making them interpretable to improve trust in these systems. In resp…
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities
Genevera I. Allen, Luqin Gan, Lili Zheng
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only p…
LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
Luqin Gan, Lili Zheng, Genevera I. Allen
Feature importance inference is critical for the interpretability and reliability of machine learning models. There has been increasing interest in developing model-agnostic approa…
Fast and Interpretable Consensus Clustering via Minipatch Learning
Luqin Gan, Genevera I. Allen
Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results. This approach ensembles…