5 papers
Cluster Analysis with Resampling for Validation and Exploration (CARVE)
Kai R. Wycik, Tiffany M. Tang, Tarek M. Zikry +1
Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are highly sensitive to the choice of…
MEDAL: Manifold Embedding Distillation via Autoencoder Learning
Irene Chang, Tarek M. Zikry, Genevera I. Allen
Low-dimensional embeddings are widely used as visual summaries of high-dimensional data and to enable downstream scientific discoveries. Yet, popular nonlinear dimension reduction…
Group-Aware Matrix Estimation and Latent Subspace Recovery
Hamza Golubovic, Matthew Shen, Genevera I. Allen +1
Modern matrix completion problems often involve heterogeneous data whose rows simultaneously belong to many meta-categories, such as demographic and age groups in recommendation sy…
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Andersen Chang, Tiffany M. Tang, Tarek M. Zikry +1
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy,…
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…