42 citations · 95 across the 11 of their papers we have counts for
11 papers
On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis
Yan Shuo Tan, Omer Ronen, Theo Saarinen +1
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performa…
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
Abhineet Agarwal, Ana M. Kenney, Yan Shuo Tan +2
Random forests (RFs) are among the most popular supervised learning algorithms due to their nonlinear flexibility and ease-of-use. However, as black box models, they can only be in…
A Mixing Time Lower Bound for a Simplified Version of BART
Omer Ronen, Theo Saarinen, Yan Shuo Tan +2
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression algorithm. The posterior is a distribution over sums of decision trees, and predictions ar…
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods
Abhineet Agarwal, Yan Shuo Tan, Omer Ronen +2
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice. To mitigate overfitting, trees are typically regularized by…
Fast Interpretable Greedy-Tree Sums
Yan Shuo Tan, Chandan Singh, Keyan Nasseri +6
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In…
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…
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval
Yan Shuo Tan, Roman Vershynin
In recent literature, a general two step procedure has been formulated for solving the problem of phase retrieval. First, a spectral technique is used to obtain a constant-error in…
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…
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods
Yan Shuo Tan, Roman Vershynin
The problem of Non-Gaussian Component Analysis (NGCA) is about finding a maximal low-dimensional subspace in so that data points projected onto follow a non-…