Fetching the paper…
Reading the bibliography…
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 predictions with minimal a priori model specifications.
Ridge regression: Biased estimation for nonorthogonal problems
Arthur E Hoerl and Robert W Kennard · 1970
Earlier work this paper cites.
Interrelation of the regression models used for structure-activity analyses
Arthur Cammarata · 1972
Earlier work this paper cites.
Hedonic housing prices and the demand for clean air
David Harrison Jr and Daniel L Rubinfeld · 1978
Earlier work this paper cites.
Classification and Regression Trees
Leo Breiman, Jerome Friedman, Charles J. Stone, and R.A. Olshen · 1984
Earlier work this paper cites.
Attributes of the performance of central processing units: A relative performance prediction model
Phillip Ein-Dor and Jacob Feldmesser · 1987
Earlier work this paper cites.
Combining instance-based and model-based learning
J Ross Quinlan · 1993
Earlier work this paper cites.
Weighted holistic invariant molecular descriptors. part 2. theory development and applications on modeling physicochemical properties of polyaromatic hydrocarbons
R Todeschini, P Gramatica, R Provenzani, and E Marengo · 1995
Earlier work this paper cites.
Extending and benchmarking Cascade-Correlation: extensions to the Cascade-Correlation architecture and benchmarking of feed-forward supervised artificial neural networks
Samuel George Waugh · 1995
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
Modeling of strength of high-performance concrete using artificial neural networks
I-C Yeh · 1998
Earlier work this paper cites.
Instance-based classification by emerging patterns
Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao · 2000
Earlier work this paper cites.
Random Forests
Leo Breiman · 2001
Earlier work this paper cites.
Molecular descriptors influencing melting point and their role in classification of solid drugs
Christel AS Bergström, Ulf Norinder, Kristina Luthman, and Per Artursson · 2003
Earlier work this paper cites.
Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of pdgfr inhibitors
Rajarshi Guha and Peter C Jurs · 2004
Earlier work this paper cites.
Assessing the reliability of a qsar model’s predictions
Linnan He and Peter C Jurs · 2005
Earlier work this paper cites.
Gene selection and classification of microarray data using random forest
Ramón Díaz-Uriarte and Sara Alvarez De Andres · 2006
Earlier work this paper cites.
Using random forests for handwritten digit recognition
Simon Bernard, Sébastien Adam, and Laurent Heutte · 2007
Cited alongside, same era.
Random forests for classification in ecology
D Richard Cutler, Thomas C Edwards Jr, Karen H Beard, Adele Cutler, Kyle T Hess, Jacob Gibson, and Joshua J Lawler · 2007
Cited alongside, same era.
Bias in random forest variable importance measures: Illustrations, sources and a solution
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn · 2007
Cited alongside, same era.
Standard errors for bagged and random forest estimators
Joseph Sexton and Petter Laake · 2009
Cited alongside, same era.
On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
Gérard Biau and Luc Devroye · 2010
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Later among the works it cites.
Extended comparisons of best subset selection, forward stepwise selection, and the lasso
Trevor Hastie, Robert Tibshirani, and Ryan J Tibshirani · 2017
Later among the works it cites.
Formal hypothesis tests for additive structure in random forests
Lucas Mentch and Giles Hooker · 2017
Later among the works it cites.
Panning for gold:‘model-x’knockoffs for high dimensional controlled variable selection
Emmanuel Candes, Yingying Fan, Lucas Janson, and Jinchi Lv · 2018
Later among the works it cites.
Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
Later among the works it cites.
Estimation and inference of heterogeneous treatment effects using random forests
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kristin K Nicodemus, James D Malley, Carolin Strobl, and Andreas Ziegler · 2010
Cited alongside, same era.
Classification with correlated features: unreliability of feature ranking and solutions
Laura Toloşi and Thomas Lengauer · 2011
Cited alongside, same era.
Event labeling combining ensemble detectors and background knowledge
Hadi Fanaee-T and Joao Gama · 2014
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.
Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Stefan Wager, Trevor Hastie, and Bradley Efron · 2014
Cited alongside, same era.
Using crowd-source based features from social media and conventional features to predict the movies popularity
Mehreen Ahmed, Maham Jahangir, Hammad Afzal, Awais Majeed, and Imran Siddiqi · 2015
Cited alongside, same era.
Controlling the false discovery rate via knockoffs
Rina Foygel Barber, Emmanuel J Candès, et al · 2015
Cited alongside, same era.
Stefan Wager and Susan Athey · 2018
Later among the works it cites.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Later among the works it cites.
Scalable and efficient hypothesis testing with random forests
Tim Coleman, Wei Peng, and Lucas Mentch · 2019
Later among the works it cites.
Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
Later among the works it cites.
Sharp analysis of a simple model for random forests
Jason M. Klusowski · 2019
Later among the works it cites.
The implicit regularization of ordinary least squares ensembles
Daniel LeJeune, Hamid Javadi, and Richard G Baraniuk · 2019
Later among the works it cites.
Wei Peng, Tim Coleman, and Lucas Mentch · 2019
Later among the works it cites.
Double trouble in double descent: Bias and variance (s) in the lazy regime
Stéphane d’Ascoli, Maria Refinetti, Giulio Biroli, and Florent Krzakala · 2020
Closest in time.
Implicit regularization of random feature models
Arthur Jacot, Berfin Şimşek, Francesco Spadaro, Clément Hongler, and Franck Gabriel · 2020
Closest in time.
The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
Dmitry Kobak, Jonathan Lomond, and Benoit Sanchez · 2020
Closest in time.
Randomization as regularization: A degrees of freedom explanation for random forest success
Lucas Mentch and Siyu Zhou · 2020
Closest in time.
A unified approach for inference on algorithm-agnostic variable importance
Brian D Williamson, Peter B Gilbert, Noah R Simon, and Marco Carone · 2020
Closest in time.