A Projection Pursuit Algorithm for Exploratory Data Analysis. - Jerome H. Friedman; John W. Tukey (IEEE Trans. Computers, 1974)

PRIM-9: An Interactive Multi-dimensional Data Display and Analysis System. - Mary Anne Fisherkeller; Jerome H. Friedman; John W. Tukey (ACM Pacific, 1975)

An Algorithm for Finding Nearest Neighbors. - Jerome H. Friedman; Forest Baskett; Leonard J. Shustek (IEEE Trans. Computers, 1975)

A Recursive Partitioning Decision Rule for Nonparametric Classification. - Jerome H. Friedman (IEEE Trans. Computers, 1977)

An Algorithm for Finding Best Matches in Logarithmic Expected Time. - Jerome H. Friedman; Jon Louis Bentley; Raphael A. Finkel (ACM Trans. Math. Softw., 1977)

Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces. - Jon Louis Bentley; Jerome H. Friedman (IEEE Trans. Computers, 1978)

Data Structures for Range Searching. - Jon Louis Bentley; Jerome H. Friedman (ACM Comput. Surv., 1979)

A Nested Partitioning Procedure for Numerical Multiple Integration. - Jerome H. Friedman; Margaret H. Wright (ACM Trans. Math. Softw., 1981)

Adaptive Spline Networks. - Jerome H. Friedman (NIPS, 1990)

Lazy Decision Trees. - Jerome H. Friedman; Ron Kohavi; Yeogirl Yun (AAAI/IAAI, Vol. 1, 1996)

On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality. - Jerome H. Friedman (Data Min. Knowl. Discov., 1997)

Bump hunting in high-dimensional data. - Jerome H. Friedman; Nicholas I. Fisher (Stat. Comput., 1999)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie; Jerome H. Friedman; Robert Tibshirani (Springer, 2001)

Note on "Comparison of Model Selection for Regression" by Vladimir Cherkassky and Yunqian Ma. - Trevor Hastie; Robert Tibshirani; Jerome H. Friedman (Neural Comput., 2003)

New Similarity Rules for Mining Data. - Vito Di Gesù; Jerome H. Friedman (WIRN/NAIS, 2005)

Recent Advances in Predictive (Machine) Learning. - Jerome H. Friedman (J. Classif., 2006)

Intruders pattern identification. - Vito Di Gesù; Giosuè Lo Bosco; Jerome H. Friedman (ICPR, 2008)

Regularization paths and coordinate descent. - Trevor Hastie; Jerome H. Friedman; Robert Tibshirani (KDD, 2008)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. - Trevor Hastie; Robert Tibshirani; Jerome H. Friedman (Springer, 2009)

Wavelet-based gradient boosting. - Eugene Dubossarsky; Jerome H. Friedman; John T. Ormerod; Matthew P. Wand (Stat. Comput., 2016)

rCOSA: A Software Package for Clustering Objects on Subsets of Attributes. - Maarten M. Kampert; Jacqueline J. Meulman; Jerome H. Friedman (J. Classif., 2017)

Expert-Augmented Machine Learning. - Efstathios D. Gennatas; Jerome H. Friedman; Lyle H. Ungar; Romain Pirracchio; Eric Eaton; L. Reichman; Yannet Interian; Charles B. Simone II; A. Auerbach; E. Delgado; Mark J. van der Laan; Timothy D. Solberg; et al. (CoRR, 2019)

Contrast Trees and Distribution Boosting. - Jerome H. Friedman (CoRR, 2019)

Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations. - Jerome H. Friedman (CoRR, 2020)

Lockout: Sparse Regularization of Neural Networks. - Gilmer Valdes; Wilmer Arbelo; Yannet Interian; Jerome H. Friedman (CoRR, 2021)

Representational Gradient Boosting: Backpropagation in the Space of Functions. - Gilmer Valdes; Jerome H. Friedman; Fei Jiang; Efstathios D. Gennatas (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Function Trees: Transparent Machine Learning. - Jerome H. Friedman (CoRR, 2024)