61 citations · 66 across the 2 of their papers we have counts for
8 papers
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Abraham J. Wyner, Matthew Olson, Justin Bleich +1
There is a large literature explaining why AdaBoost is a successful classifier. The literature on AdaBoost focuses on classifier margins and boosting's interpretation as the optimi…
Using Regression Kernels to Forecast A Failure to Appear in Court
Richard Berk, Justin Bleich, Adam Kapelner +3
Forecasts of prospective criminal behavior have long been an important feature of many criminal justice decisions. There is now substantial evidence that machine learning procedure…
Evaluating the Effectiveness of Personalized Medicine with Software
Adam Kapelner, Justin Bleich, Alina Levine +3
We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves th…
Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity
Justin Bleich, Adam Kapelner
We incorporate heteroskedasticity into Bayesian Additive Regression Trees (BART) by modeling the log of the error variance parameter as a linear function of prespecified covariates…
bartMachine: Machine Learning with Bayesian Additive Regression Trees
Adam Kapelner, Justin Bleich
We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selecti…
Variable selection for BART: An application to gene regulation
Justin Bleich, Adam Kapelner, Edward I. George +1
We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels.…