Fetching the paper…
Reading the bibliography…
Decision trees are important both as interpretable models amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient boosting.
Problems in the analysis of survey data, and a proposal
James N Morgan and John A Sonquist · 1963
Earlier work this paper cites.
The behavior of maximum likelihood estimates under nonstandard conditions
Peter J Huber · 1967
Earlier work this paper cites.
Consistent nonparametric regression
Charles J Stone · 1977
Earlier work this paper cites.
Robustness in the strategy of scientific model building
George EP Box · 1979
Earlier work this paper cites.
Abstract Inference
U. Grenander · 1981
Earlier work this paper cites.
Nonparametric maximum likelihood estimation by the method of sieves
Stuart Geman and Chii-Ruey Hwang · 1982
Earlier work this paper cites.
Optimal global rates of convergence for nonparametric regression
Charles J Stone · 1982
Earlier work this paper cites.
Classification and Regression Trees
Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen · 1984
Earlier work this paper cites.
Generalized additive models
Trevor Hastie and Robert Tibshirani · 1986
Earlier work this paper cites.
Penalized discriminant analysis
Trevor Hastie, Andreas Buja, and Robert Tibshirani · 1995
Earlier work this paper cites.
A classification tree approach to the development of actuarial violence risk assessment tools
Henry J Steadman, Eric Silver, John Monahan, Paul Appelbaum, Pamela Clark Robbins, Edward P Mulvey, Thomas Grisso, Loren H Roth, and Steven Banks · 2000
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
The Elements of Statistical Learning , volume 1
Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al · 2001
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
An empirical comparison of supervised learning algorithms
Rich Caruana and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
An empirical evaluation of supervised learning in high dimensions
Rich Caruana, Nikos Karampatziakis, and Ainur Yessenalina · 2008
Earlier work this paper cites.
Predictive learning via rule ensembles
Jerome H Friedman and Bogdan E Popescu · 2008
Earlier work this paper cites.
Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study
Nathan Kuppermann, James F Holmes, Peter S Dayan, John D Hoyle, Shireen M Atabaki, Richard Holubkov, Frances M Nadel, David Monroe, Rachel M Stanley, Dominic A Borgialli, et al · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Cited alongside, same era.
Analysis of a random forests model
Gérard Biau · 2012
Cited alongside, same era.
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
Anne-Laure Boulesteix, Silke Janitza, Jochen Kruppa, and Inke R König · 2012
Cited alongside, same era.
Random forests for genomic data analysis
Xi Chen and Hemant Ishwaran · 2012
Cited alongside, same era.
Elements of Information Theory
T.M. Cover and J.A. Thomas · 2012
Cited alongside, same era.
Minimax-optimal rates for sparse additive models over kernel classes via convex programming
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2012
Data-driven advice for applying machine learning to bioinformatics problems
Randal S Olson, William La Cava, Zairah Mustahsan, Akshay Varik, and Jason H Moore · 2018
Later among the works it cites.
When do random forests fail?
Cheng Tang, Damien Garreau, and Ulrike von Luxburg · 2018
Later among the works it cites.
Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2018
Later among the works it cites.
Models as approximations I: Consequences illustrated with linear regression
Andreas Buja, Lawrence Brown, Richard Berk, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang, and Linda Zhao · 2019
Later among the works it cites.
Linear aggregation in tree-based estimators
Sören R Künzel, Theo F Saarinen, Edward W Liu, and Jasjeet S Sekhon · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim · 2014
Cited alongside, same era.
Fifty years of classification and regression trees
Wei-Yin Loh · 2014
Cited alongside, same era.
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, and David Madigan · 2015
Cited alongside, same era.
Consistency of random forests
Erwan Scornet, Gérard Biau, and Jean-Philippe Vert · 2015
Cited alongside, same era.
Recursive partitioning for heterogeneous causal effects
Susan Athey and Guido Imbens · 2016
Cited alongside, same era.
A random forest guided tour
Gérard Biau and Erwan Scornet · 2016
Cited alongside, same era.
Definitions, methods, and applications in interpretable machine learning
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
Later among the works it cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Later among the works it cites.
Additive models with trend filtering
Veeranjaneyulu Sadhanala and Ryan J Tibshirani · 2019
Later among the works it cites.
Local linear forests
Rina Friedberg, Julie Tibshirani, Susan Athey, and Stefan Wager · 2020
Later among the works it cites.
Sparse learning with cart
Jason Klusowski · 2020
Later among the works it cites.
Generalized and scalable optimal sparse decision trees
Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, and Margo Seltzer · 2020
Later among the works it cites.
Trees, forests, and impurity-based variable importance
Erwan Scornet · 2020
Later among the works it cites.
Estimation and inference with trees and forests in high dimensions
Vasilis Syrgkanis and Manolis Zampetakis · 2020
Later among the works it cites.
Strong optimal classification trees
Sina Aghaei, Andrés Gómez, and Phebe Vayanos · 2021
Closest in time.
Provable boolean interaction recovery from tree ensemble obtained via random forests
Merle Behr, Yu Wang, Xiao Li, and Bin Yu · 2021
Closest in time.
Bridging Breiman’s brook: From algorithmic modeling to statistical learning
Giles Hooker and Lucas Mentch · 2021
Closest in time.
Universal consistency of decision trees in high dimensions
Jason M. Klusowski · 2021
Closest in time.
Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong · 2021
Closest in time.