Awesome Random Forest Save

Random Forest - a curated list of resources regarding random forest

Project README

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.


Please feel free to pull requests.

The project is not actively maintained.

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Table of Contents

  • [Codes] (#codes)
  • Theory
    • Lectures
    • Books
    • [Papers] (#papers)
      • [Analysis / Understanding] (#analysis--understanding)
      • [Model variants] (#model-variants)
    • [Thesis] (#thesis)
  • [Applications] (#applications)
    • [Image Classification] (#image-classification)
    • [Object Detection] (#object-detection)
    • [Object Tracking] (#object-tracking)
    • [Edge Detection] (#edge-detection)
    • [Semantic Segmentation] (#semantic-segmentation)
    • [Human / Hand Pose Estimation] (#human--hand-pose-estimation)
    • [3D Localization] (#3d-localization)
    • [Low-Level Vision] (#low-level-vision)
    • [Facial Expression Recognition] (#facial-expression-recognition)
    • [Interpretability, regularization, compression pruning and feature selection](#Interpretability, regularization, compression pruning and feature selection)






Analysis / Understanding

  • Consistency of random forests [Paper]
  • Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests, The Annals of Statistics, in press.
  • On the asymptotics of random forests [Paper]
  • Scornet, E. (2015). On the asymptotics of random forests, Journal of Multivariate Analysis, in press.
  • Random Forests In Theory and In Practice [[Paper] (]
    • Misha Denil, David Matheson, Nando de Freitas, Narrowing the Gap: Random Forests In Theory and In Practice, ICML 2014
  • Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease [Paper]

Model variants


  • Understanding Random Forests
  • PhD dissertation, Gilles Louppe, July 2014. Defended on October 9, 2014.
  • [Repository] with thesis and related codes


Image classification

Object Detection

Object Tracking

Edge Detection

Semantic Segmentation

Human / Hand Pose Estimation

3D localization

  • Imperial College London [[Paper] (]
    • Alykhan Tejani, Danhang Tang, Rigas Kouskouridas, and Tae-Kyun Kim, Latent-Class Hough Forests for 3D Object Detection and Pose Estimation, ECCV 2014
  • Microsoft Research Cambridge + University of Illinois + Imperial College London [[Paper] (]
    • 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] (]
    • 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

Facial expression recognition

  • Sorbonne Universites [Paper]
    • Arnaud Dapogny, Kevin Bailly, and Severine Dubuisson, Pairwise Conditional Random Forests for Facial Expression Recognition, ICCV 2015

Interpretability, regularization, compression pruning and feature selection

  • Global Refinement of Random Forest [[Paper] (]
    • 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]
  • Feature-Budgeted Random Forest [[Paper] (] [Supp]
    • 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]
  • Meinshausen, Nicolai. "Node harvest." The Annals of Applied Statistics 4.4 (2010): 2049-2072. [Paper] [Code R] [Code Python]
  • Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach S. Hara, K. Hayashi, [Paper] [Code]
  • Cui, Zhicheng, et al. "Optimal action extraction for random forests and boosted trees." ACM SIGKDD 2015. [Paper]
  • DART: Dropouts meet Multiple Additive Regression Trees K. V. Rashmi, Ran Gilad-Bachrach [Paper]
  • Begon, Jean-Michel, Arnaud Joly, and Pierre Geurts. Joint learning and pruning of decision forests. (2016). [Paper]

Maintainers - Jiwon Kim, Jung Kwon Lee

Open Source Agenda is not affiliated with "Awesome Random Forest" Project. README Source: kjw0612/awesome-random-forest
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