1.9k citations · 3.6k across the 13 of their papers we have counts for
15 papers
Lockout: Sparse Regularization of Neural Networks
Gilmer Valdes, Wilmer Arbelo, Yannet Interian +1
Many regression and classification procedures fit a parameterized function of predictor variables to data based on some loss criterion .…
Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations
Jerome H. Friedman
The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vect…
Contrast Trees and Distribution Boosting
Jerome H. Friedman
Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer progra…
Expert-Augmented Machine Learning
E. D. Gennatas, J. H. Friedman, L. H. Ungar +10
Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of t…
Principal component-guided sparse regression
J. Kenneth Tay, Jerome Friedman, Robert Tibshirani
We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations. The method combines th…
A Pliable Lasso
Robert Tibshirani, Jerome Friedman
We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of modifying variables. These modifiers might be variables such a…