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Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang, Haiyang Huang, Cynthia Rudin +1
Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension that has always…
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning
Qinwen Huang, Ye Zhou, Xiaochen Du +4
Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution. The low…
Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do about It
Marco Morucci, Cynthia Rudin
Many major works in social science employ matching to make causal conclusions, but different matches on the same data may produce different treatment effect estimates, even when th…
Bandits for BMO Functions
Tianyu Wang, Cynthia Rudin
We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are…
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu +2
Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning. Despite efforts over the past 40…
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Caroline Wang, Bin Han, Bhrij Patel +1
Objectives: We study interpretable recidivism prediction using machine learning (ML) models and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike p…