17 citations · 30 across the 3 of their papers we have counts for
4 papers
Locally Optimized Random Forests
Tim Coleman, Kimberly Kaufeld, Mary Frances Dorn +1
Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, th…
Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statistics
Wei Peng, Tim Coleman, Lucas Mentch
Random forests remain among the most popular off-the-shelf supervised learning algorithms. Despite their well-documented empirical success, however, until recently, few theoretical…
Scalable and Efficient Hypothesis Testing with Random Forests
Tim Coleman, Wei Peng, Lucas Mentch
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made the…
Statistical Inference on Tree Swallow Migrations with Random Forests
Tim Coleman, Lucas Mentch, Daniel Fink +4
Bird species' migratory patterns have typically been studied through individual observations and historical records. In recent years however, the eBird citizen science project, whi…