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 Awesome Random Forest
 Awesome Random Forest
Random Forest - a curated list of resources regarding tree-based methods and more, including but not limited to random forest, bagging and boosting.
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Theory
Lectures
⟡ ICCV 2013 Tutorial : Decision Forests and Fields for Computer Vision (http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/) by Jamie Shotton and Sebastian Nowozin
  ⟡ Lecture 1 (http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/) : Randomized Decision Forests and their Applications in Computer Vision I (Decision 
Forest, Classification Forest, 
  ⟡ Lecture 2 (http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/) : Randomized Decision Forests and their Applications in Computer Vision II (Regression Forest, Decision Jungle)
  ⟡ Lecture 1 (http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/) : Randomized Decision Forests and their Applications in Computer 
Vision I (Decision Forest, Classification Forest, 
  ⟡ Lecture 2 (http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/) : Randomized Decision Forests and their Applications in Computer Vision II (Regression Forest, 
Decision Jungle)
  ⟡ Lecture 3 (http://techtalks.tv/talks/entropy-estimation-and-streaming-data-sebastian/59433/) : Entropy estimation and streaming data
  ⟡ Lecture 4 (http://techtalks.tv/talks/decision-and-regression-tree-fields-sebastian/59435/) : Decision and Regression Tree Fields
⟡ UBC Machine Learning (http://www.cs.ubc.ca/~nando/540-2013/lectures.html) by Nando de Freitas
  ⟡ Lecture 8 slide (http://www.cs.ubc.ca/~nando/540-2013/lectures/l8.pdf) , Lecture 8 video (https://www.youtube.com/watch?v=-dCtJjlEEgM&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=11) : Decision trees
  ⟡ Lecture 9 slide (http://www.cs.ubc.ca/~nando/540-2013/lectures/l9.pdf) , Lecture 9 video (https://www.youtube.com/watch?v=3kYujfDgmNk&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=12) : Random forests
  ⟡ Lecture 8 slide (http://www.cs.ubc.ca/~nando/540-2013/lectures/l8.pdf) , Lecture 8 video (https://www.youtube.com/watch?v=-dCtJjlEEgM&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=11) :
Decision trees
  ⟡ Lecture 9 slide (http://www.cs.ubc.ca/~nando/540-2013/lectures/l9.pdf) , Lecture 9 video (https://www.youtube.com/watch?v=3kYujfDgmNk&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=12) :
Random forests
  ⟡ Lecture 10 video (https://www.youtube.com/watch?v=zFGPjRPwyFw&index=13&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6) : Random forest applications
 
Books
@@ -92,10 +95,11 @@
⟡ Canonical Correlation Forests Paper (http://arxiv.org/pdf/1507.05444.pdf) 
  ⟡ Tom Rainforth, and Frank Wood, Canonical Correlation Forests, arxiv 2015
⟡ Relating Cascaded Random Forests to Deep Convolutional Neural Networks Paper (http://arxiv.org/pdf/1507.07583.pdf) 
  ⟡ David L Richmond, Dagmar Kainmueller, Michael Y Yang, Eugene W Myers, and Carsten Rother, Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation, arxiv 2015
  ⟡ David L Richmond, Dagmar Kainmueller, Michael Y Yang, Eugene W Myers, and Carsten Rother, Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation,
arxiv 2015
⟡ Bayesian Forests Paper (http://jmlr.org/proceedings/papers/v37/matthew15.pdf) 
  ⟡ Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle, Bayesian and Empirical Bayesian Forests, ICML 2015
⟡ Mondrian Forests: Efficient Online Random Forests Paper  (http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_nips14.pdf) Code  (http://www.gatsby.ucl.ac.uk/~balaji/mondrianforest/) Slides  
⟡ Mondrian Forests: Efficient Online Random Forests Paper  (http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_nips14.pdf) Code  (http://www.gatsby.ucl.ac.uk/~balaji/mondrianforest/) Slides
(http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_slides.pdf)
  ⟡ Balaji Lakshminarayanan, Daniel M. Roy and Yee Whye Teh, Mondrian Forests: Efficient Online Random Forests, NIPS 2014
⟡ Extremely randomized trees P Geurts, D Ernst, L Wehenkel - Machine learning, 2006 Paper (http://orbi.ulg.be/bitstream/2268/9357/1/geurts-mlj-advance.pdf) Code 
@@ -104,7 +108,8 @@
  ⟡ Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models for Classification, NIPS 2013
  ⟡ Laptev, Dmitry, and Joachim M. Buhmann. Transformation-invariant convolutional jungles. CVPR 2015. Paper 
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Laptev_Transformation-Invariant_Convolutional_Jungles_2015_CVPR_paper.pdf) 
⟡ Semi-supervised Node Splitting for Random Forest Construction Paper (http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf) 
⟡ Semi-supervised Node Splitting for Random Forest Construction Paper 
(http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf) 
  ⟡ Xiao Liu, Mingli Song, Dacheng Tao, Zicheng Liu, Luming Zhang, Chun Chen and Jiajun Bu, Semi-supervised Node Splitting for Random Forest Construction, CVPR 2013
⟡ Improved Information Gain Estimates for Decision Tree Induction Paper (http://www.nowozin.net/sebastian/papers/nowozin2012infogain.pdf) 
  ⟡ Sebastian Nowozin, Improved Information Gain Estimates for Decision Tree Induction, ICML 2012
@@ -151,11 +156,13 @@
  ⟡ Martin Godec, Peter M. Roth, and Horst Bischof, Hough-based Tracking of Non-Rigid Objects, ICCV 2011
Edge Detection
⟡ University of California, Irvine Paper (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf) Code (https://github.com/samhallman/oef) 
⟡ University of California, Irvine Paper (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf) Code 
(https://github.com/samhallman/oef) 
  ⟡ Sam Hallman, and Charless C. Fowlkes, Oriented Edge Forests for Boundary Detection, CVPR 2015
⟡ Microsoft Research Paper (http://research-srv.microsoft.com/pubs/202540/DollarICCV13edges.pdf) Code (https://github.com/pdollar/edges) 
  ⟡ Piotr Dollar, and C. Lawrence Zitnick, Structured Forests for Fast Edge Detection, ICCV 2013
⟡ Massachusetts Inst. of Technology + Microsoft Research Paper (http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf) Code (https://github.com/joelimlimit/SketchTokens) 
⟡ Massachusetts Inst. of Technology + Microsoft Research Paper (http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf) Code 
(https://github.com/joelimlimit/SketchTokens) 
  ⟡ Joseph J. Lim, C. Lawrence Zitnick, and Piotr Dollar, Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection, CVPR 2013
Semantic Segmentation
@@ -164,15 +171,16 @@
⟡ INRIA + Microsoft Research Cambridge Paper (http://step.polymtl.ca/~rv101/MICCAI-Laplacian-Forest.pdf) 
  ⟡ Herve Lombaert, Darko Zikic, Antonio Criminisi, and Nicholas Ayache, Laplacian Forests:Semantic Image Segmentation by Guided Bagging, MICCAI 2014
⟡ Microsoft Research Cambridge + GE Global Research Center + University of California + Rutgers Univeristy Paper (http://research.microsoft.com/pubs/146430/criminisi_ipmi_2011c.pdf) 
  ⟡ Albert Montillo1, Jamie Shotton, John Winn, Juan Eugenio Iglesias, Dimitri Metaxas, and Antonio Criminisi, Entangled Decision Forests and their Application for Semantic Segmentation of CT Images, IPMI 2011
  ⟡ Albert Montillo1, Jamie Shotton, John Winn, Juan Eugenio Iglesias, Dimitri Metaxas, and Antonio Criminisi, Entangled Decision Forests and their Application for Semantic Segmentation of CT
Images, IPMI 2011
⟡ University of Cambridge + Toshiba Corporate R&D Center Paper (http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-semantic-texton-forests.pdf) 
  ⟡ Jamie Shotton, Matthew Johnson, and Roberto Cipolla, Semantic Texton Forests for Image Categorization and Segmentation, CVPR 2008
 
Human / Hand Pose Estimation
⟡ Microsoft Research Cambridge Paper-CHI (http://research.microsoft.com/pubs/238453/pn362-sharp.pdf) Video-CHI (http://research.microsoft.com/pubs/238453/pn362-sharp-video.mp4) 
 ****Paper-CVPR** (http://research.microsoft.com/pubs/162510/vm.pdf)** 
  ⟡ Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew 
Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time Hand Tracking, CHI 2015
  ⟡ Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal 
Krupka, Andrew Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time Hand Tracking, CHI 2015
  ⟡ Jonathan Taylor, Jamie Shotton, Toby Sharp, and Andrew Fitzgibbon, The Vitruvian Manifold:Inferring Dense Correspondences for One-Shot Human Pose Estimation, CVPR 2012
⟡ Microsoft Research Haifa Paper (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Krupka_Discriminative_Ferns_Ensemble_2014_CVPR_paper.pdf) 
  ⟡ Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, and Daniel Freedman, Discriminative Ferns Ensemble for Hand Pose Recognition, CVPR 2014
@@ -193,19 +201,21 @@
⟡ Microsoft Research Cambridge + University of Illinois + Imperial College London Paper (http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf) 
  ⟡ Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, and Shahram Izadi, Multi-Output Learning for Camera Relocalization, CVPR 2014
⟡ Microsoft Research Cambridge Paper (http://research.microsoft.com/pubs/184826/relocforests.pdf) 
  ⟡ Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images, CVPR 2013
  ⟡ Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images, CVPR 
2013
Low-Level vision
⟡ Super-Resolution
  ⟡ Technicolor R&I Hannover Paper 
(https://technicolor-my.sharepoint.com/personal/jordi_salvador_technicolor_com/_layouts/15/guestaccess.aspx?guestaccesstoken=2z88Le9arMQ7tcGGYApHmdM9Pet2AqqoxMBDcu6eRbc%3d&docid=0e7f0b9ed1d0f4497829ae6b2b0de
eec3) 
(https://technicolor-my.sharepoint.com/personal/jordi_salvador_technicolor_com/_layouts/15/guestaccess.aspx?guestaccesstoken=2z88Le9arMQ7tcGGYApHmdM9Pet2AqqoxMBDcu6eRbc%3d&docid=0e7f0b9ed
1d0f4497829ae6b2b0deeec3) 
* Jordi Salvador, and Eduardo Pérez-Pellitero, Naive Bayes Super-Resolution Forest, ICCV 2015
  ⟡ Graz University of Technology Paper (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schulter_Fast_and_Accurate_2015_CVPR_paper.pdf) 
* Samuel Schulter, Christian Leistner, and Horst Bischof, Fast and Accurate Image Upscaling with Super-Resolution Forests, CVPR 2015
⟡ Denoising 
  ⟡ Microsoft Research + iCub Facility - Istituto Italiano di Tecnologia Paper (http://research.microsoft.com/pubs/217099/CVPR2014ForestFiltering.pdf) 
* Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, and Tim Paek, Filter Forests for Learning Data-Dependent Convolutional Kernels, CVPR 2014
* Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, and Tim Paek, Filter Forests for Learning Data-Dependent Convolutional Kernels 
, CVPR 2014 
Facial expression recognition
⟡ Sorbonne Universites Paper (http://www.isir.upmc.fr/files/2015ACTI3549.pdf) 
@@ -214,10 +224,12 @@
Interpretability, regularization, compression pruning and feature selection
⟡ Global Refinement of Random Forest Paper (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf) 
  ⟡ Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun, Global Refinement of Random Forest, CVPR 2015
⟡ L1-based compression of random forest models Arnaud Joly, Fran¸cois Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 Paper (https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2012-43.pdf) 
⟡ L1-based compression of random forest models Arnaud Joly, Fran¸cois Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 Paper 
(https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2012-43.pdf) 
⟡ Feature-Budgeted Random Forest Paper (http://jmlr.org/proceedings/papers/v37/nan15.pdf) Supp (http://jmlr.org/proceedings/papers/v37/nan15-supp.pdf) 
  ⟡ Feng Nan, Joseph Wang, Venkatesh Saligrama, Feature-Budgeted Random Forest, ICML 2015 
  ⟡ Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama NIPS 2016 Paper (https://papers.nips.cc/paper/6250-pruning-random-forests-for-prediction-on-a-budget.pdf) 
  ⟡ Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama NIPS 2016 Paper 
(https://papers.nips.cc/paper/6250-pruning-random-forests-for-prediction-on-a-budget.pdf) 
⟡ Meinshausen, Nicolai. "Node harvest." The Annals of Applied Statistics 4.4 (2010): 2049-2072. Paper (http://projecteuclid.org/download/pdfview_1/euclid.aoas/1294167809) Code R 
(https://cran.r-project.org/web/packages/nodeHarvest/index.html) Code Python (https://github.com/mbillingr/NodeHarvest) 
⟡ Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach S. Hara, K. Hayashi, Paper (https://arxiv.org/abs/1606.09066) Code (https://github.com/sato9hara/defragTrees)