151 citations · 263 across the 16 of their papers we have counts for
18 papers
Bias, Consistency, and Alternative Perspectives of the Infinitesimal Jackknife
Wei Peng, Lucas Mentch, Leonard Stefanski
Though introduced nearly 50 years ago, the infinitesimal jackknife (IJ) remains a popular modern tool for quantifying predictive uncertainty in complex estimation settings. In part…
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest
Siyu Zhou, Lucas Mentch
Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists. D…
Forward Stability and Model Path Selection
Nicholas Kissel, Lucas Mentch
Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates…
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning
Lucas Mentch, Giles Hooker
In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures. Twenty years later this division feels far more ephemeral, both in terms of assi…
Posterior Calibrated Training on Sentence Classification Tasks
Taehee Jung, Dongyeop Kang, Hua Cheng +2
Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In m…
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance
Lucas Mentch, Siyu Zhou
As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predicti…