Jerome H Friedman
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Jerome H Friedman's AcademicInfluence.com Rankings
Jerome H Friedmanmathematics Degrees
Mathematics
#2541
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#3899
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Statistics
#99
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#130
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Measure Theory
#132
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#208
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Mathematics
Jerome H Friedman's Degrees
- PhD Statistics Stanford University
- Masters Mathematics Stanford University
- Bachelors Mathematics University of California, Berkeley
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(Suggest an Edit or Addition)Jerome H Friedman's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Greedy function approximation: A gradient boosting machine. (2001) (16639)
- Classification and Regression Trees (1984) (14579)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition (2005) (14017)
- Regularization Paths for Generalized Linear Models via Coordinate Descent. (2010) (13067)
- The Elements of Statistical Learning (2001) (11421)
- Special Invited Paper-Additive logistic regression: A statistical view of boosting (2000) (5554)
- Sparse inverse covariance estimation with the graphical lasso. (2008) (5083)
- Stochastic gradient boosting (2002) (4906)
- Multivariate Adaptive Regression Splines (1991) (3810)
- An Algorithm for Finding Best Matches in Logarithmic Expected Time (1976) (3087)
- Regularized Discriminant Analysis (1989) (2418)
- Projection Pursuit Regression (1981) (2282)
- A Statistical View of Some Chemometrics Regression Tools (1993) (2275)
- PATHWISE COORDINATE OPTIMIZATION (2007) (2020)
- Estimating Optimal Transformations for Multiple Regression and Correlation. (1985) (1720)
- A Projection Pursuit Algorithm for Exploratory Data Analysis (1974) (1702)
- Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. (2011) (1468)
- On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality (2004) (958)
- Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By (2000) (950)
- PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008) (856)
- A note on the group lasso and a sparse group lasso (2010) (793)
- Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests (1979) (685)
- Data Structures for Range Searching (1979) (658)
- Multiple additive regression trees with application in epidemiology (2003) (636)
- Bump hunting in high-dimensional data (1999) (598)
- Strong rules for discarding predictors in lasso‐type problems (2010) (536)
- Exploratory Projection Pursuit (1987) (534)
- Predicting Multivariate Responses in Multiple Linear Regression (1997) (495)
- [A Statistical View of Some Chemometrics Regression Tools]: Response (1993) (484)
- SparseNet: Coordinate Descent With Nonconvex Penalties (2011) (476)
- FLEXIBLE PARSIMONIOUS SMOOTHING AND ADDITIVE MODELING (1989) (456)
- An Algorithm for Finding Nearest Neighbors (1975) (441)
- A Recursive Partitioning Decision Rule for Nonparametric Classification (1977) (408)
- Clustering objects on subsets of attributes (with discussion) (2004) (406)
- A VARIABLE SPAN SMOOTHER (1984) (403)
- New Insights and Faster Computations for the Graphical Lasso (2011) (330)
- An introduction to multivariate adaptive regression splines (1995) (320)
- Lazy Decision Trees (1996) (302)
- Flexible Metric Nearest Neighbor Classification (1994) (274)
- PROJECTION PURSUIT DENSITY ESTIMATION (1984) (265)
- Clustering objects on subsets of attributes (2002) (235)
- An Overview of Predictive Learning and Function Approximation (1994) (232)
- Fast sparse regression and classification (2012) (232)
- Model Assessment and Selection (2009) (213)
- Overview of Supervised Learning (2001) (213)
- On bagging and nonlinear estimation (2007) (196)
- The elements of statistical learning. 2001 (2001) (195)
- A Study of Error Variance Estimation in Lasso Regression (2013) (194)
- DATA MINING AND STATISTICS: WHAT''S THE CONNECTION (1997) (189)
- Boosting and Additive Trees (2009) (181)
- From Statistics to Neural Networks: Theory and Pattern Recognition Applications (1996) (175)
- The Monotone Smoothing of Scatterplots (1984) (161)
- Classification and Regression Trees (Wadsworth Statistics/Probability) (1984) (153)
- PRIM-9: An Interactive Multi-dimensional Data Display and Analysis System (1975) (150)
- Additive Logistic Regression : a Statistical View ofBoostingJerome (1998) (145)
- Importance Sampled Learning Ensembles (2003) (124)
- Cr-Pyrope Garnets in the Lithospheric Mantle. I. Compositional Systematics and Relations to Tectonic Setting (1999) (124)
- Recent Advances in Predictive (Machine) Learning (2006) (121)
- Applications of the lasso and grouped lasso to the estimation of sparse graphical models (2010) (116)
- Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces (1978) (116)
- Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.0-2] (2020) (113)
- Linear Methods for Regression (2001) (113)
- A New Graph-Based Two-Sample Test for Multivariate and Object Data (2013) (111)
- The II P method for estimating multivariate functions from noisy data (1991) (105)
- Rejoinder: Multivariate Adaptive Regression Splines (1991) (101)
- Graph-Theoretic Measures of Multivariate Association and Prediction (1983) (99)
- Multidimensional Additive Spline Approximation (1983) (84)
- From Statistics to Neural Networks (1994) (84)
- Gradient Directed Regularization (2004) (80)
- Classification: Oldtimers and newcomers (1989) (80)
- A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression (2013) (77)
- Sparse inverse covariance estimation with the lasso (2007) (76)
- On Multivariate Goodness-of-Fit and Two-Sample Testing (2004) (71)
- Graphics for the Multivariate Two-Sample Problem (1981) (71)
- Estimating Optimal Transformations for Multiple Regression and Correlation: Rejoinder (1985) (67)
- Smoothing of Scatterplots (1982) (65)
- Estimating Functions of Mixed Ordinal and Categorical Variables Using Adaptive Splines (1991) (63)
- The Role of Statistics in the Data Revolution? (2001) (62)
- Additive Models, Trees, and Related Methods (2009) (61)
- SMART User's Guide (1984) (57)
- A tree-structured approach to nonparametric multiple regression (1979) (56)
- Expert-augmented machine learning (2019) (53)
- Building more accurate decision trees with the additive tree (2019) (52)
- 1999 REITZ LECTURE GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE' (2001) (48)
- Linear Methods for Classification (2001) (43)
- Support Vector Machines and Flexible Discriminants (2009) (43)
- Classification and Multiple Regression through Projection Pursuit (1985) (41)
- A Nested Partitioning Procedure for Numerical Multiple Integration (1981) (35)
- Observations of overturning in the upper mesosphere and lower thermosphere and the implications for enhanced winds (2004) (35)
- Applications of a new subspace clustering algorithm (COSA) in medical systems biology (2007) (34)
- Discussion of Boosting Papers (2003) (33)
- Optimal reduced-rank quadratic classifiers using the Fukunaga-Koontz transform with applications to automated target recognition (2003) (32)
- A survey of algorithms and data structures for range searching (1978) (30)
- High-Dimensional Problems: p N (2009) (29)
- Projection Pursuit Methods for Data Analysis. (1982) (28)
- Kernel Smoothing Methods (2009) (28)
- Statistical techniques for the classification of chromites in diamond exploration samples (1997) (27)
- A Pliable Lasso (2017) (27)
- M AND N PLOTS (1983) (23)
- Tree-Structured Classification Via Generalized Discriminant Analysis: Comment (1988) (23)
- An interactive multidimensional data display and analysis system (1974) (23)
- John W. Tukey's work on interactive graphics (2002) (22)
- Diagnostics and extrapolation in machine learning (2004) (21)
- Adaptive Spline Networks (1990) (21)
- Basis Expansions and Regularization (2001) (20)
- Tutorial: Getting Started with MART in R (2002) (18)
- Model Inference and Averaging (2009) (16)
- Prototype Methods and Nearest-Neighbors (2009) (16)
- An introduction to real-time graphical techniques for analyzing multivariate data (1987) (13)
- Principal component‐guided sparse regression (2018) (13)
- [The ∏ Method for Estimating Multivariate Functions from Noisy Data]: Discussion (1991) (13)
- Contrast trees and distribution boosting (2019) (12)
- Note on Comparison of Model Selection for Regression by Vladimir Cherkassky and Yunqian Ma (2003) (12)
- Wavelet-based gradient boosting (2016) (12)
- Ensemble learning for prediction (2004) (10)
- A NONPARAMETRIC PROCEDURE FOR COMPARING MULTIVARIATE POINT SETS (2007) (9)
- Graphical Methods of Exploratory Data Analysis (1985) (9)
- Introduction To Tree Classification (2017) (9)
- HARDWARE FOR KINEMATIC STATISTICAL GRAPHICS. (1981) (8)
- Comment: Classifier Technology and the Illusion of Progress (2006) (8)
- Regularization paths and coordinate descent (2008) (7)
- Discussions of boosting papers, and rejoinders (2004) (7)
- Separating Signal From Background Using Ensembles of Rules (2006) (7)
- Approaches to analysis of data that concentrate near higher-dimensional manifolds (1979) (6)
- Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008 (2008) (5)
- rCOSA: A Software Package for Clustering Objects on Subsets of Attributes (2016) (4)
- [Flexible Parsimonious Smoothing and Additive Modeling]: Response (1989) (4)
- Intruders pattern identification (2008) (3)
- Undirected Graphical Models (2009) (3)
- Two papers on range searching (1978) (3)
- Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron (2020) (3)
- New Similarity Rules for Mining Data (2005) (3)
- Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations (2020) (2)
- An Adaptive Importance Sampling Procedure. (1981) (2)
- Panel Discussion on Data Analysis Trends in X-Ray and γ-Ray Astronomy 30/5/84, 11°°–12°° (1985) (2)
- Panel Discussion on Data Analysis Trends in X-Ray and Gamma-Ray Astronomy (1986) (1)
- Discussion of \Evidence contrary to the statistical view of boosting" (2007) (1)
- Lockout: Sparse Regularization of Neural Networks (2021) (1)
- Real Time Graphical Techniques for Analyzing Multivariate Data (1982) (1)
- Discussion: Projection Pursuit (1985) (1)
- Construction of Trees from a Learning Sample (2017) (1)
- New Developments in COSA: Clustering Objects on Subsets of Attributes (2004) (1)
- Statistician's view of data analysis (1986) (1)
- Right Sized Trees and Honest Estimates (2017) (1)
- Stability of relativistic stars and black holes. (1988) (1)
- A VARUBLE SPAN SMOOTHER* (1984) (1)
- Multivariate Adaptive Regression Splines (Preprint) (1990) (1)
- rCOSA: A Software Package for Clustering Objects on Subsets of Attributes (2017) (0)
- Remembering Leo (2011) (0)
- 4 Discussion and Future Work 3 Experiments and Results 2 Our Solution Adaptive Sentence Boundary Disambiguation (1994) (0)
- Data analysis in astronomy II. Proceedings of the second international workshop, held at Erice, Sicily, Italy, 17 - 30 April 1986. (1986) (0)
- Panel Discussion: Systems for Data Analysis What they AEE; what they Could be? (1985) (0)
- Panel Discussion on “ how can Computer Science Contribute to the Solution of Problems Posed by Astronomers ?” (1985) (0)
- Geometric Learning Algorithms Geometric Learning Algorithms (1990) (0)
- Reply to Nock and Nielsen: On the work of Nock and Nielsen and its relationship to the additive tree (2020) (0)
- Representational Gradient Boosting: Backpropagation in the Space of Functions (2021) (0)
- Mass Spectra Classification (2017) (0)
- Final Technical Reports 15 June 1983 through 31 March 1986 on Contract N00014-83-K-0472, (1987) (0)
- A RELATIVELY EFFICIRNT ALGORITHM - FOR FINDING NEAREST NEIGHEQRS* (1974) (0)
- Bayes Rules and Partitions (2017) (0)
- New Results from the RV instrument, Exoplanet Tracker, at the KPNO 2.1m (2003) (0)
- Technology. His Current Research Interests In- Clude Neural Computation, Statistical Learning Theory, and Handwriting Recognition. Kwok and Yeung: Constructive Algorithms for Structure Learning \some Approximation Properties of Projection Pursuit Learning Networks," in Advances in Neural Information (1997) (0)
- ntruders pattern identificatiolll (2008) (0)
- Review ID: 9116 (2001) (0)
- Slac Pub-3013 Stan-orion 003 July 1982 (ml Smoothing of Scatterplots* (1999) (0)
- [Developments in Linear Regression Methodology: 1959-1982]: Discussion (1983) (0)
- Searching for structure in multivariate data (1976) (0)
- New Graph-Based Two-Sample Tests for Multivariate Distributions (2013) (0)
- -1.-REMARKS ON THE EXISTENCE OF Two z(1660) RESONANCES (2012) (0)
- Metal Layers at High Altitudes and Near the Polar Summer Mesopause (2003) (0)
- Medical Diagnosis and Prognosis (2017) (0)
- A new graph-based test comparing two multivariate distributions (2013) (0)
- Strengthening and Interpreting (2017) (0)
- Prediction of Secondary School Students’ Alcohol Addiction using Random Forest (2017) (0)
- Panel Discussion on Trends in Optical and Radio Data Analysis (1985) (0)
- Book-Review - Data Analysis in Astronomy - Erice - 1984 (1987) (0)
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Jerome H Friedman is affiliated with the following schools:
