Fetching the paper…
Reading the bibliography…
Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression.
Optimal global rates of convergence for nonparametric regression
Stone, C. J. (1982) · 1982
Earlier work this paper cites.
Induction of decision trees
Quinlan, J. R. (1986) · 1986
Earlier work this paper cites.
Incremental induction of decision trees
Utgoff, P. E. (1989) · 1989
Earlier work this paper cites.
Sequential weighting algorithms for multi-alphabet sources
Tjalkens, T. J., Y. M. Shtarkov, and F. M. J. Willems (1993) · 1993
Earlier work this paper cites.
The context-tree weighting method: Basic properties
Willems, F. M. J., Y. M. Shtarkov, and T. J. Tjalkens (1995, May) · 1995
Earlier work this paper cites.
Predicting nearly as well as the best pruning of a decision tree
Helmbold, D. P. and R. E. Schapire (1997) · 1997
Earlier work this paper cites.
Bayesian CART model search
Chipman, H. A., E. I. George, and R. E. McCulloch (1998) · 1998
Earlier work this paper cites.
A bayesian CART algorithm
Denison, D. G. T., B. K. Mallick, and A. F. M. Smith (1998) · 1998
Earlier work this paper cites.
A game of prediction with expert advice
Vovk, V. (1998) · 1998
Earlier work this paper cites.
The context-tree weighting method: Extensions
Willems, F. M. J. (1998, Mar) · 1998
Earlier work this paper cites.
The “progressive mixture” estimator for regression trees
Blanchard, G. (1999) · 1999
Earlier work this paper cites.
Mining high-speed data streams
Domingos, P. and G. Hulten (2000) · 2000
Earlier work this paper cites.
Random forests
Breiman, L. (2001) · 2001
Earlier work this paper cites.
Statistical Learning Theory and Stochastic Optimization: Ecole d’Eté de Probabilités de Saint-Flour XXXI - 2001
Catoni, O. (2004) · 2001
Earlier work this paper cites.
Prediction, Learning, and Games
Cesa-Bianchi, N. and G. Lugosi (2006) · 2006
Earlier work this paper cites.
Gene selection and classification of microarray data using random forest
Díaz-Uriarte, R. and S. A. De Andres (2006) · 2006
Cited alongside, same era.
Extremely randomized trees
Geurts, P., D. Ernst, and L. Wehenkel (2006) · 2006
Cited alongside, same era.
The Mondrian process
Roy, D. M. and Y. W. Teh (2009) · 2009
Cited alongside, same era.
On-line random forests
Saffari, A., C. Leistner, J. Santner, M. Godec, and H. Bischof (2009) · 2009
Cited alongside, same era.
BART: Bayesian additive regression trees
Chipman, H. A., E. I. George, and R. E. McCulloch (2010) · 2010
Cited alongside, same era.
Party: A laboratory for recursive partytioning
Hothorn, T., K. Hornik, C. Strobl, and A. Zeileis (2010) · 2010
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Skip context tree switching
Bellemare, M., J. Veness, and E. Talvitie (2014) · 2014
Later among the works it cites.
Mondrian forests: Efficient online random forests
Lakshminarayanan, B., D. M. Roy, and Y. W. Teh (2014) · 2014
Later among the works it cites.
Understanding random forests: From theory to practice
Louppe, G. (2014) · 2014
Later among the works it cites.
Consistency of random forests
Scornet, E., G. Biau, and J.-P. Vert (2015, 08) · 2015
Later among the works it cites.
Adaptive concentration of regression trees, with application to random forests
Wager, S. and G. Walther (2015) · 2015
Later among the works it cites.
Mondrian forests for large-scale regression when uncertainty matters
Lakshminarayanan, B., D. M. Roy, and Y. W. Teh (2016) · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay (2011) · 2011
Cited alongside, same era.
Computability, inference and modeling in probabilistic programming
Roy, D. M. (2011) · 2011
Cited alongside, same era.
Dynamic trees for learning and design
Taddy, M. A., R. B. Gramacy, and N. G. Polson (2011) · 2011
Cited alongside, same era.
Analysis of a random forests model
Biau, G. (2012) · 2012
Cited alongside, same era.
Random forests for genomic data analysis
Chen, X. and H. Ishwaran (2012) · 2012
Cited alongside, same era.
Variance reduction in purely random forests
Genuer, R. (2012) · 2012
Cited alongside, same era.
UCI machine learning repository
Dua, D. and C. Graff (2017) · 2017
Later among the works it cites.
Universal consistency and minimax rates for online Mondrian forests
Mourtada, J., S. Gaïffas, and E. Scornet (2017) · 2017
Later among the works it cites.
Posterior concentration for bayesian regression trees and their ensembles
Rockova, V. and S. van der Pas (2017) · 2017
Later among the works it cites.
Impact of subsampling and tree depth on random forests
Duroux, R. and E. Scornet (2018) · 2018
Later among the works it cites.
Bayesian regression tree ensembles that adapt to smoothness and sparsity
Linero, A. R. and Y. Yang (2018) · 2018
Later among the works it cites.
Minimax optimal rates for Mondrian trees and forests
Mourtada, J., S. Gaïffas, and E. Scornet (2018) · 2018
Later among the works it cites.
Consistency of random forests and other averaging classifiers
Biau, G., L. Devroye, and G. Lugosi (2008) · 2033
Closest in time.
On the generalization ability of on-line learning algorithms
Cesa-Bianchi, N., A. Conconi, and C. Gentile (2004, Sept) · 2057
Closest in time.