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pro vyhledávání: '"Tan, Yan Shuo"'
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 performance is supported by theoretical guarantees that its posterior distribu
Externí odkaz:
http://arxiv.org/abs/2406.19958
Stacking regressions is an ensemble technique that forms linear combinations of different regression estimators to enhance predictive accuracy. The conventional approach uses cross-validation data to generate predictions from the constituent estimato
Externí odkaz:
http://arxiv.org/abs/2309.09880
Mean decrease in impurity (MDI) is a popular feature importance measure for random forests (RFs). We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on t
Externí odkaz:
http://arxiv.org/abs/2307.01932
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 are made by averaging approximate samples from the posterior. The combin
Externí odkaz:
http://arxiv.org/abs/2210.09352
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 a variety of techniques that modify their structure (e.g. pruning). We
Externí odkaz:
http://arxiv.org/abs/2202.00858
Autor:
Tan, Yan Shuo, Singh, Chandan, Nasseri, Keyan, Agarwal, Abhineet, Duncan, James, Ronen, Omer, Epland, Matthew, Kornblith, Aaron, Yu, Bin
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision t
Externí odkaz:
http://arxiv.org/abs/2201.11931
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 boosting. Their statistical properties, however, are not well understood
Externí odkaz:
http://arxiv.org/abs/2110.09626
Autor:
Dwivedi, Raaz, Tan, Yan Shuo, Park, Briton, Wei, Mian, Horgan, Kevin, Madigan, David, Yu, Bin
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), with large heterogeneous treatment effects. StaDISC was developed d
Externí odkaz:
http://arxiv.org/abs/2008.10109
Autor:
Altieri, Nick, Barter, Rebecca L., Duncan, James, Dwivedi, Raaz, Kumbier, Karl, Li, Xiao, Netzorg, Robert, Park, Briton, Singh, Chandan, Tan, Yan Shuo, Tang, Tiffany, Wang, Yu, Zhang, Chao, Yu, Bin
Publikováno v:
Published in Harvard Data Science Review, 2020
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 curation of a large data repository containing COVID-19 information from a ra
Externí odkaz:
http://arxiv.org/abs/2005.07882
Autor:
Tan, Yan Shuo, Vershynin, Roman
Publikováno v:
Journal of Machine Learning Research, 24(58), 1-47 (2023)
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 initial estimate, following which, the estimate is refined to arbitrary
Externí odkaz:
http://arxiv.org/abs/1910.12837