25 citations · 48 across the 14 of their papers we have counts for
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stat.ML2018★ 2 cited
A Minimax Surrogate Loss Approach to Conditional Difference Estimation
Siong Thye Goh, Cynthia Rudin
We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. To overcome the problem th…
stat.ML2015
Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
Siong Thye Goh, Lesia Semenova, Cynthia Rudin
We present sparse tree-based and list-based density estimation methods for binary/categorical data. Our density estimation models are higher dimensional analogies to variable bin w…
stat.ML2014★ 3 cited
Box Drawings for Learning with Imbalanced Data
Siong Thye Goh, Cynthia Rudin
The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We…