6 citations · 15 across the 5 of their papers we have counts for
4 papers
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
Yan Shuo Tan, Abhineet Agarwal, Bin Yu
Decision trees are important both as interpretable models amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient bo…
Stable discovery of interpretable subgroups via calibration in causal studies
Raaz Dwivedi, Yan Shuo Tan, Briton Park +4
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC…
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
Nick Altieri, Rebecca L. Barter, James Duncan +11
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curatio…
Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis
Yan Shuo Tan
We consider the problem of high-dimensional misspecified phase retrieval. This is where we have an -sparse signal vector in , which we wish to recov…