5 papers
The Rashomon Effect for Visualizing High-Dimensional Data
Yiyang Sun, Haiyang Huang, Gaurav Rajesh Parikh +1
Dimension reduction (DR) is inherently non-unique: multiple embeddings can preserve the structure of high-dimensional data equally well while differing in layout or geometry. In th…
Trustworthy Feature Importance Avoids Unrestricted Permutations
Emanuele Borgonovo, Francesco Cappelli, Xuefei Lu +2
Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable importance approaches. We propose…
Dimension Reduction with Locally Adjusted Graphs
Yingfan Wang, Yiyang Sun, Haiyang Huang +1
Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptom…
Improving Decision Sparsity
Yiyang Sun, Tong Wang, Cynthia Rudin
Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it u…
Navigating the Effect of Parametrization for Dimensionality Reduction
Haiyang Huang, Yingfan Wang, Cynthia Rudin
Parametric dimensionality reduction methods have gained prominence for their ability to generalize to unseen datasets, an advantage that traditional approaches typically lack. Desp…