[38;5;12m                                                                                   [39m[38;2;255;187;0m[1m[4mAwesome Decision, Classification, and Regression Tree Research Papers[0m
[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m (https://github.com/sindresorhus/awesome)[39m
[38;5;14m[1m![0m[38;5;12mPRs Welcome[39m[38;5;14m[1m (https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)[0m[38;5;12m (http://makeapullrequest.com)[39m
[38;5;14m[1m![0m[38;5;12mrepo size[39m[38;5;14m[1m (https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-decision-tree-papers.svg)[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-decision-tree-papers/archive/master.zip)[39m
[38;5;12m [39m[38;5;12m![39m[38;5;14m[1mLicense[0m[38;5;12m [39m[38;5;12m(https://img.shields.io/github/license/benedekrozemberczki/awesome-decision-tree-papers.svg?color=blue)[39m[38;5;12m [39m[38;5;14m[1m![0m[38;5;12mbenedekrozemberczki[39m[38;5;14m[1m [0m[38;5;14m[1m(https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)[0m[38;5;12m [39m
[38;5;12m(https://twitter.com/intent/follow?screen_name=benrozemberczki)[39m

[38;5;12m  [39m


[38;5;12mA curated list of classification and regression tree research papers with implementations from the following conferences:[39m

[38;5;12m- Machine learning[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeurIPS[0m[38;5;12m (https://nips.cc/) [39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICML[0m[38;5;12m (https://icml.cc/) [39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICLR[0m[38;5;12m (https://iclr.cc/)[39m
[38;5;12m- Computer vision[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCVPR[0m[38;5;12m (http://cvpr2019.thecvf.com/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICCV[0m[38;5;12m (http://iccv2019.thecvf.com/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mECCV[0m[38;5;12m (https://eccv2018.org/)[39m
[38;5;12m- Natural language processing[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mACL[0m[38;5;12m (http://www.acl2019.org/EN/index.xhtml)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNAACL[0m[38;5;12m (https://naacl2019.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEMNLP[0m[38;5;12m (https://www.emnlp-ijcnlp2019.org/) [39m
[38;5;12m- Data[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKDD[0m[38;5;12m (https://www.kdd.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCIKM[0m[38;5;12m (http://www.cikmconference.org/)   [39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICDM[0m[38;5;12m (http://icdm2019.bigke.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSDM[0m[38;5;12m (https://www.siam.org/Conferences/CM/Conference/sdm19)   [39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPAKDD[0m[38;5;12m (http://pakdd2019.medmeeting.org)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPKDD/ECML[0m[38;5;12m (http://ecmlpkdd2019.org)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSIGIR[0m[38;5;12m (https://sigir.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWWW[0m[38;5;12m (https://www2019.thewebconf.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWSDM[0m[38;5;12m (www.wsdm-conference.org) [39m
[38;5;12m- Artificial intelligence[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAAAI[0m[38;5;12m (https://www.aaai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAISTATS[0m[38;5;12m (https://www.aistats.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICANN[0m[38;5;12m (https://e-nns.org/icann2019/)   [39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIJCAI[0m[38;5;12m (https://www.ijcai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUAI[0m[38;5;12m (http://www.auai.org/)[39m

[38;5;12mSimilar[39m[38;5;12m [39m[38;5;12mcollections[39m[38;5;12m [39m[38;5;12mabout[39m[38;5;12m [39m[38;5;14m[1mgraph[0m[38;5;14m[1m [0m[38;5;14m[1mclassification[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-graph-classification),[39m[38;5;12m [39m[38;5;14m[1mgradient[0m[38;5;14m[1m [0m[38;5;14m[1mboosting[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers),[39m[38;5;12m [39m[38;5;14m[1mfraud[0m[38;5;14m[1m [0m[38;5;14m[1mdetection[0m[38;5;12m [39m
[38;5;12m(https://github.com/benedekrozemberczki/awesome-fraud-detection-papers),[39m[38;5;12m [39m[38;5;14m[1mMonte[0m[38;5;14m[1m [0m[38;5;14m[1mCarlo[0m[38;5;14m[1m [0m[38;5;14m[1mtree[0m[38;5;14m[1m [0m[38;5;14m[1msearch[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers),[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;14m[1mcommunity[0m[38;5;14m[1m [0m[38;5;14m[1mdetection[0m[38;5;12m [39m
[38;5;12m(https://github.com/benedekrozemberczki/awesome-community-detection)[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mimplementations.[39m

[38;2;255;187;0m[4m2022[0m

[38;5;12m- [39m[38;5;14m[1mUsing MaxSAT for Efficient Explanations of Tree Ensembles (AAAI 2022)[0m
[38;5;12m  - Alexey Ignatiev, Yacine Izza, Peter J. Stuckey, João Marques-Silva[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://alexeyignatiev.github.io/assets/pdf/iisms-aaai22-preprint.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles (AAAI 2022)[0m
[38;5;12m  - Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://a-lucic.github.io/talks/ICML_SMRL_focus.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mExplainable and Local Correction of Classification Models Using Decision Trees (AAAI 2022)[0m
[38;5;12m  - Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Keisuke Goto, Yuta Fujishige, Satoshi Hara[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/20816)[39m

[38;5;12m- [39m[38;5;14m[1mRobust Optimal Classification Trees against Adversarial Examples (AAAI 2022)[0m
[38;5;12m  - Daniël Vos, Sicco Verwer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2109.03857)[39m

[38;5;12m- [39m[38;5;14m[1mFairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values (AAAI 2022)[0m
[38;5;12m  - Haewon Jeong, Hao Wang, Flávio P. Calmon[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2109.10431)[39m

[38;5;12m- [39m[38;5;14m[1mFast Sparse Decision Tree Optimization via Reference Ensembles (AAAI 2022)[0m
[38;5;12m  - Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2112.00798)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://pypi.org/project/gosdt/)[39m

[38;5;12m- [39m[38;5;14m[1mTransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)[0m
[38;5;12m  - Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2112.02365)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/yihengsun/TransBoost)[39m

[38;5;12m- [39m[38;5;14m[1mCounterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees (AISTATS 2022)[0m
[38;5;12m  - Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://proceedings.mlr.press/v151/kanamori22a.html)[39m

[38;5;12m- [39m[38;5;14m[1mAccurate Shapley Values for explaining tree-based models (AISTATS 2022)[0m
[38;5;12m  - Salim I. Amoukou, Tangi Salaün, Nicolas J.-B. Brunel[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2106.03820)[39m

[38;5;12m- [39m[38;5;14m[1mA cautionary tale on fitting decision trees to data from additive models: generalization lower bounds (AISTATS 2022)[0m
[38;5;12m  - Yan Shuo Tan, Abhineet Agarwal, Bin Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2110.09626)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/aagarwal1996/additive_trees)[39m

[38;5;12m- [39m[38;5;14m[1mEnterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees (WWW 2022)[0m
[38;5;12m  - Philip A. Etter, Kai Zhong, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2106.02697)[39m

[38;5;12m- [39m[38;5;14m[1mMBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration (WWW 2022)[0m
[38;5;12m  - Siguang Huang, Yunli Wang, Lili Mou, Huayue Zhang, Han Zhu, Chuan Yu, Bo Zheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2202.04348)[39m

[38;5;12m- [39m[38;5;14m[1mRethinking Conversational Recommendations: Is Decision Tree All You Need (CIKM 2022)[0m
[38;5;12m  - A S. M. Ahsan-Ul-Haque, Hongning Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2208.14614)[39m

[38;5;12m- [39m[38;5;14m[1mA Neural Tangent Kernel Perspective of Infinite Tree Ensembles (ICLR 2022)[0m
[38;5;12m  - Ryuichi Kanoh, Mahito Sugiyama[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/forum?id=vUH85MOXO7h)[39m
[38;5;12m [39m
[38;5;12m- [39m[38;5;14m[1mPOETREE: Interpretable Policy Learning with Adaptive Decision Trees (ICLR 2022)[0m
[38;5;12m  - Alizée Pace, Alex Chan, Mihaela van der Schaar[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2203.08057)[39m

[38;5;12m- [39m[38;5;14m[1mHierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models (ICML 2022)[0m
[38;5;12m  - Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2202.00858)[39m

[38;5;12m- [39m[38;5;14m[1mPopular decision tree algorithms are provably noise tolerant (ICML 2022)[0m
[38;5;12m  - Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2206.08899)[39m

[38;5;12m- [39m[38;5;14m[1mRobust Counterfactual Explanations for Tree-Based Ensembles (ICML 2022)[0m
[38;5;12m  - Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://proceedings.mlr.press/v162/dutta22a.html)[39m

[38;5;12m- [39m[38;5;14m[1mFast Provably Robust Decision Trees and Boosting (ICML 2022)[0m
[38;5;12m  - Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://proceedings.mlr.press/v162/guo22h.html)[39m

[38;5;12m- [39m[38;5;14m[1mBAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression (ICML 2022)[0m
[38;5;12m  - Zhao Tang Luo, Huiyan Sang, Bani K. Mallick[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://proceedings.mlr.press/v162/luo22a.html)[39m

[38;5;12m- [39m[38;5;14m[1mQuant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features (ICML 2022)[0m
[38;5;12m  - Rahul Mazumder, Xiang Meng, Haoyue Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2206.11844)[39m

[38;5;12m- [39m[38;5;14m[1mA Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources (ICML 2022)[0m
[38;5;12m  - Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2103.06261)[39m

[38;5;12m- [39m[38;5;14m[1mOn Preferred Abductive Explanations for Decision Trees and Random Forests (IJCAI 2022)[0m
[38;5;12m  - Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2022/0091.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mExtending Decision Tree to Handle Multiple Fairness Criteria (IJCAI 2022)[0m
[38;5;12m  - Alessandro Castelnovo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2022/0822.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFlexible Modeling and Multitask Learning using Differentiable Tree Ensembles (KDD 2022)[0m
[38;5;12m  - Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2205.09717)[39m

[38;5;12m- [39m[38;5;14m[1mIntegrity Authentication in Tree Models (KDD 2022)[0m
[38;5;12m  - Weijie Zhao, Yingjie Lao, Ping Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3534678.3539428)[39m

[38;5;12m- [39m[38;5;14m[1mRetrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)[0m
[38;5;12m  - Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3534678.3539052)[39m

[38;5;12m- [39m[38;5;14m[1mImproved feature importance computation for tree models based on the Banzhaf value (UAI 2022)[0m
[38;5;12m  - Adam Karczmarz, Tomasz Michalak, Anish Mukherjee, Piotr Sankowski, Piotr Wygocki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://proceedings.mlr.press/v180/karczmarz22a.html)[39m

[38;5;12m- [39m[38;5;14m[1mLearning linear non-Gaussian polytree models (UAI 2022)[0m
[38;5;12m  - Daniele Tramontano, Anthea Monod, Mathias Drton[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2208.06701)[39m

[38;2;255;187;0m[4m2021[0m

[38;5;12m- [39m[38;5;14m[1mOnline Probabilistic Label Trees (AISTATS 2021)[0m
[38;5;12m  - Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczyński[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2007.04451)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mwydmuch/napkinXC)[39m

[38;5;12m- [39m[38;5;14m[1mOptimal Decision Trees for Nonlinear Metrics (AAAI 2021)[0m
[38;5;12m  - Emir Demirovic, Peter J. Stuckey[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2009.06921)[39m

[38;5;12m- [39m[38;5;14m[1mSAT-based Decision Tree Learning for Large Data Sets (AAAI 2021)[0m
[38;5;12m  - André Schidler, Stefan Szeider[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/16509)[39m

[38;5;12m- [39m[38;5;14m[1mParameterized Complexity of Small Decision Tree Learning (AAAI 2021)[0m
[38;5;12m  - Sebastian Ordyniak, Stefan Szeider[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ac.tuwien.ac.at/files/tr/ac-tr-21-002.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCounterfactual Explanations for Oblique Decision Trees: Exact - Efficient Algorithms (AAAI 2021)[0m
[38;5;12m  - Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2103.01096)[39m

[38;5;12m- [39m[38;5;14m[1mGeometric Heuristics for Transfer Learning in Decision Trees (CIKM 2021)[0m
[38;5;12m  - Siddhesh Chaubal, Mateusz Rzepecki, Patrick K. Nicholson, Guangyuan Piao, Alessandra Sala[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3459637.3482259)[39m

[38;5;12m- [39m[38;5;14m[1mFairness-Aware Training of Decision Trees by Abstract Interpretation (CIKM 2021)[0m
[38;5;12m  - Francesco Ranzato, Caterina Urban, Marco Zanella[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3459637.3482342)[39m

[38;5;12m- [39m[38;5;14m[1mEnabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification (CIKM 2021)[0m
[38;5;12m  - Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2106.00730)[39m

[38;5;12m- [39m[38;5;14m[1mAre Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)[0m
[38;5;12m  - Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/forum?id=Ut1vF_q_vC)[39m

[38;5;12m- [39m[38;5;14m[1mNBDT: Neural-Backed Decision Tree (ICLR 2021)[0m
[38;5;12m  - Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2004.00221)[39m

[38;5;12m- [39m[38;5;14m[1mVersatile Verification of Tree Ensembles (ICML 2021)[0m
[38;5;12m  - Laurens Devos, Wannes Meert, Jesse Davis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2010.13880)[39m

[38;5;12m- [39m[38;5;14m[1mConnecting Interpretability and Robustness in Decision Trees through Separation (ICML 2021)[0m
[38;5;12m  - Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2102.07048)[39m

[38;5;12m- [39m[38;5;14m[1mOptimal Counterfactual Explanations in Tree Ensembles (ICML 2021)[0m
[38;5;12m  - Axel Parmentier, Thibaut Vidal[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2106.06631)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Training of Robust Decision Trees Against Adversarial Examples (ICML 2021)[0m
[38;5;12m  - Daniël Vos, Sicco Verwer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2012.10438)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Binary Decision Trees by Argmin Differentiation (ICML 2021)[0m
[38;5;12m  - Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/2010.04627.pdf)[39m
[38;5;12m [39m
[38;5;12m- [39m[38;5;14m[1mBLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment (KDD 2021)[0m
[38;5;12m  - Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua T. Vogelstein, Randal Burns[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3447548.3467368)[39m

[38;5;12m- [39m[38;5;14m[1mControlBurn: Feature Selection by Sparse Forests (KDD 2021)[0m
[38;5;12m  - Brian Liu, Miaolan Xie, Madeleine Udell[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3447548.3467387?sid=SCITRUS)[39m

[38;5;12m- [39m[38;5;14m[1mProbabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)[0m
[38;5;12m  - Olivier Sprangers, Sebastian Schelter, Maarten de Rijke[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3447548.3467278)[39m

[38;5;12m- [39m[38;5;14m[1mVerifying Tree Ensembles by Reasoning about Potential Instances (SDM 2021)[0m
[38;5;12m  - Laurens Devos, Wannes Meert, Jesse Davis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2001.11905)[39m

[38;2;255;187;0m[4m2020[0m

[38;5;12m- [39m[38;5;14m[1mDTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (ACL 2020)[0m
[38;5;12m  - Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, Ambreen Nazir[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2004.13455)[39m

[38;5;12m- [39m[38;5;14m[1mPrivacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)[0m
[38;5;12m  - Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1911.04209)[39m

[38;5;12m- [39m[38;5;14m[1mPractical Federated Gradient Boosting Decision Trees (AAAI 2020)[0m
[38;5;12m  - Qinbin Li, Zeyi Wen, Bingsheng He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1911.04206)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Inference of Optimal Decision Trees (AAAI 2020)[0m
[38;5;12m  - Florent Avellaneda[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://florent.avellaneda.free.fr/dl/AAAI20.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/FlorentAvellaneda/InferDT)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Optimal Decision Trees Using Caching Branch-and-Bound Search (AAAI 2020)[0m
[38;5;12m  - Gael Aglin, Siegfried Nijssen, Pierre Schaus[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A223390/datastream/PDF_01/view)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://pypi.org/project/dl8.5/)[39m

[38;5;12m- [39m[38;5;14m[1mAbstract Interpretation of Decision Tree Ensemble Classifiers (AAAI 2020)[0m
[38;5;12m  - Francesco Ranzato, Marco Zanella[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.math.unipd.it/~ranzato/papers/aaai20.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/abstract-machine-learning/silva)[39m

[38;5;12m- [39m[38;5;14m[1mScalable Feature Selection for (Multitask) Gradient Boosted Trees (AISTATS 2020)[0m
[38;5;12m  - Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v108/han20a.html)[39m

[38;5;12m- [39m[38;5;14m[1mOptimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning (AISTATS 2020)[0m
[38;5;12m  - Andrew Silva, Matthew C. Gombolay, Taylor W. Killian, Ivan Dario Jimenez Jimenez, Sung-Hyun Son[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1903.09338)[39m

[38;5;12m- [39m[38;5;14m[1mExploiting Categorical Structure Using Tree-Based Methods (AISTATS 2020)[0m
[38;5;12m  - Brian Lucena[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2004.07383)[39m

[38;5;12m- [39m[38;5;14m[1mLdSM: Logarithm-depth Streaming Multi-label Decision Trees (AISTATS 2020)[0m
[38;5;12m  - Maryam Majzoubi, Anna Choromanska[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1905.10428)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mOblique Decision Trees from Derivatives of ReLU Networks (ICLR 2020)[0m
[38;5;12m  - Guang-He Lee, Tommi S. Jaakkola[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/pdf?id=Bke8UR4FPB)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/guanghelee/iclr20-lcn)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mProvable Guarantees for Decision Tree Induction: the Agnostic Setting (ICML 2020)[0m
[38;5;12m  - Guy Blanc, Jane Lange, Li-Yang Tan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2006.00743v1)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Trees for Decision-Making under the Predict-then-Optimize Framework (ICML 2020)[0m
[38;5;12m  - Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2003.00360)[39m

[38;5;12m- [39m[38;5;14m[1mThe Tree Ensemble Layer: Differentiability meets Conditional Computation (ICML 2020)[0m
[38;5;12m  - Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2002.07772)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/google-research/google-research/tree/master/tf_trees)[39m

[38;5;12m- [39m[38;5;14m[1mGeneralized and Scalable Optimal Sparse Decision Trees (ICML 2020)[0m
[38;5;12m  - Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2006.08690)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xiyanghu/OSDT)[39m

[38;5;12m- [39m[38;5;14m[1mBorn-Again Tree Ensembles (ICML 2020)[0m
[38;5;12m  - Thibaut Vidal, Maximilian Schiffer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2003.11132)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/vidalt/BA-Trees)[39m

[38;5;12m- [39m[38;5;14m[1mOn Lp-norm Robustness of Ensemble Decision Stumps and Trees (ICML 2020)[0m
[38;5;12m  - Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2008.08755)[39m

[38;5;12m- [39m[38;5;14m[1mSmaller, More Accurate Regression Forests Using Tree Alternating Optimization (ICML 2020)[0m
[38;5;12m  - Arman Zharmagambetov, Miguel Á. Carreira-Perpinan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v119/zharmagambetov20a.html)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mLearning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)[0m
[38;5;12m  - Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/163)[39m

[38;5;12m- [39m[38;5;14m[1mSpeeding up Very Fast Decision Tree with Low Computational Cost (IJCAI 2020)[0m
[38;5;12m  - Jian Sun, Hongyu Jia, Bo Hu, Xiao Huang, Hao Zhang, Hai Wan, Xibin Zhao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/0177.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mPyDL8.5: a Library for Learning Optimal Decision Trees (IJCAI 2020)[0m
[38;5;12m  - Gaël Aglin, Siegfried Nijssen, Pierre Schaus[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/0750.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/aia-uclouvain/pydl8.5)[39m

[38;5;12m- [39m[38;5;14m[1mGeodesic Forests (KDD 2020)[0m
[38;5;12m  - Meghana Madhyastha, Gongkai Li, Veronika Strnadova-Neeley, James Browne, Joshua T. Vogelstein, Randal Burns[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/pdf/10.1145/3394486.3403094)[39m

[38;5;12m- [39m[38;5;14m[1mA Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (NeurIPS 2020)[0m
[38;5;12m  - Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2011.03375)[39m

[38;5;12m- [39m[38;5;14m[1mEstimating Decision Tree Learnability with Polylogarithmic Sample Complexity (NeurIPS 2020)[0m
[38;5;12m  - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2011.01584)[39m
[38;5;12m [39m
[38;5;12m- [39m[38;5;14m[1mUniversal Guarantees for Decision Tree Induction via a Higher-Order Splitting Criterion (NeurIPS 2020)[0m
[38;5;12m  - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2010.08633)[39m

[38;5;12m- [39m[38;5;14m[1mSmooth And Consistent Probabilistic Regression Trees (NeurIPS 2020)[0m
[38;5;12m  - Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Éric Gaussier, Georges Oppenheim[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAn Efficient Adversarial Attack for Tree Ensembles (NeurIPS 2020)[0m
[38;5;12m  - Chong Zhang, Huan Zhang, Cho-Jui Hsieh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2010.11598)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/chong-z/tree-ensemble-attack)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Trees as Partitioning Machines to Characterize their Generalization Properties (NeurIPS 2020)[0m
[38;5;12m  - Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/2020/file/d2a10b0bd670e442b1d3caa3fbf9e695-Paper.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mEvidence Weighted Tree Ensembles for Text Classification (SIGIR 2020)[0m
[38;5;12m  - Md. Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu, Wei Kang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3397271.3401229)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m2019[0m

[38;5;12m- [39m[38;5;14m[1mMulti Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System (AAAI 2019)[0m
[38;5;12m  - Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1805.09484.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mInduction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)[0m
[38;5;12m  - Farhad Shakerin, Gopal Gupta[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1808.00629)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mLearning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (AAAI 2019)[0m
[38;5;12m  - Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1903.10598)[39m

[38;5;12m- [39m[38;5;14m[1mDesiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals (AAAI 2019)[0m
[38;5;12m  - Kacper Sokol, Peter A. Flach[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ojs/index.php/AAAI/article/view/5154)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mWeighted Oblique Decision Trees (AAAI 2019)[0m
[38;5;12m  - Bin-Bin Yang, Song-Qing Shen, Wei Gao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ojs/index.php/AAAI/article/view/4505)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Optimal Classification Trees Using a Binary Linear Program Formulation (AAAI 2019)[0m
[38;5;12m  - Sicco Verwer, Yingqian Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://yingqianzhang.net/wp-content/uploads/2018/12/VerwerZhangAAAI-final.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOptimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees (AAAI 2019)[0m
[38;5;12m  - Pooya Tavallali, Peyman Tavallali, Mukesh Singhal[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ojs/index.php/AAAI/article/view/4447/4325)[39m

[38;5;12m- [39m[38;5;14m[1mXBART: Accelerated Bayesian Additive Regression Trees (AISTATS 2019)[0m
[38;5;12m  - Jingyu He, Saar Yalov, P. Richard Hahn[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1810.02215)[39m

[38;5;12m- [39m[38;5;14m[1mInteraction Detection with Bayesian Decision Tree Ensembles (AISTATS 2019)[0m
[38;5;12m  - Junliang Du, Antonio R. Linero[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1809.08524)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mAdversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)[0m
[38;5;12m  - Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.dais.unive.it/~calzavara/papers/cikm19.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mInterpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)[0m
[38;5;12m  - Ya-Lin Zhang, Longfei Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3357384.3358072)[39m

[38;5;12m- [39m[38;5;14m[1mInterpreting CNNs via Decision Trees (CVPR 2019)[0m
[38;5;12m  - Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.00121)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mEDiT: Interpreting Ensemble Models via Compact Soft Decision Trees (ICDM 2019)[0m
[38;5;12m  - Jaemin Yoo, Lee Sael[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/leesael/EDiT/blob/master/docs/YooS19.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/leesael/EDiT)[39m

[38;5;12m- [39m[38;5;14m[1mFair Adversarial Gradient Tree Boosting (ICDM 2019)[0m
[38;5;12m  - Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1911.05369)[39m

[38;5;12m- [39m[38;5;14m[1mFunctional Transparency for Structured Data: a Game-Theoretic Approach (ICML 2019)[0m
[38;5;12m  - Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v97/lee19b/lee19b.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mIncorporating Grouping Information into Bayesian Decision Tree Ensembles (ICML 2019)[0m
[38;5;12m  - Junliang Du, Antonio R. Linero[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v97/du19d.html)[39m

[38;5;12m- [39m[38;5;14m[1mAdaptive Neural Trees (ICML 2019)[0m
[38;5;12m  - Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1807.06699)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/rtanno21609/AdaptiveNeuralTrees)[39m

[38;5;12m- [39m[38;5;14m[1mRobust Decision Trees Against Adversarial Examples (ICML 2019)[0m
[38;5;12m  - Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1902.10660)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/chenhongge/RobustTrees)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mLearn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples (IJCAI 2019)[0m
[38;5;12m  - Ariyam Das, Jin Wang, Sahil M. Gandhi, Jae Lee, Wei Wang, Carlo Zaniolo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2019/0306.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFAHT: An Adaptive Fairness-aware Decision Tree Classifier (IJCAI 2019)[0m
[38;5;12m  - Wenbin Zhang, Eirini Ntoutsi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1907.07237)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/vanbanTruong/FAHT)[39m

[38;5;12m- [39m[38;5;14m[1mInter-node Hellinger Distance based Decision Tree (IJCAI 2019)[0m
[38;5;12m  - Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2019/0272.pdf)[39m
[38;5;12m  - [39m[38;5;12mMatlab Code[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ZDanielsResearch/HellingerTreesMatlab)[39m
[38;5;12m  - [39m[38;5;12mR Code[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/kaustubhrpatil/HDDT)[39m

[38;5;12m- [39m[38;5;14m[1mGradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)[0m
[38;5;12m  - Yu Shi, Jian Li, Zhize Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.05640)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/GBDT-PL/GBDT-PL)[39m

[38;5;12m- [39m[38;5;14m[1mA Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees (IJCAI 2019)[0m
[38;5;12m  - Klaus Broelemann, Gjergji Kasneci[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1809.09703)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCombining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (KDD 2019)[0m
[38;5;12m  - Pan Li, Zhen Qin, Xuanhui Wang, Donald Metzler[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ai.google/research/pubs/pub48133/)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mTight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers (NeurIPS 2019)[0m
[38;5;12m  - Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/8737-tight-certificates-of-adversarial-robustness-for-randomly-smoothed-classifiers.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/guanghelee/Randomized_Smoothing)[39m

[38;5;12m- [39m[38;5;14m[1mPartitioning Structure Learning for Segmented Linear Regression Trees (NeurIPS 2019)[0m
[38;5;12m  - Xiangyu Zheng, Song Xi Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/8494-partitioning-structure-learning-for-segmented-linear-regression-trees)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mProvably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)[0m
[38;5;12m  - Maksym Andriushchenko, Matthias Hein[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1906.03526)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/max-andr/provably-robust-boosting)[39m

[38;5;12m- [39m[38;5;14m[1mOptimal Decision Tree with Noisy Outcomes (NeurIPS 2019)[0m
[38;5;12m  - Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R. Ravi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/8592-optimal-decision-tree-with-noisy-outcomes.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/sjia1/ODT-with-noisy-outcomes)[39m

[38;5;12m- [39m[38;5;14m[1mRegularized Gradient Boosting (NeurIPS 2019)[0m
[38;5;12m  - Corinna Cortes, Mehryar Mohri, Dmitry Storcheus[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/8784-regularized-gradient-boosting.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOptimal Sparse Decision Trees (NeurIPS 2019)[0m
[38;5;12m  - Xiyang Hu, Cynthia Rudin, Margo Seltzer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/8947-optimal-sparse-decision-trees.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xiyanghu/OSDT)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mMonoForest framework for tree ensemble analysis (NeurIPS 2019)[0m
[38;5;12m  - Igor Kuralenok, Vasilii Ershov, Igor Labutin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/9530-monoforest-framework-for-tree-ensemble-analysis)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xiyanghu/OSDT)[39m

[38;5;12m- [39m[38;5;14m[1mCalibrating Probability Estimation Trees using Venn-Abers Predictors (SDM 2019)[0m
[38;5;12m  - Ulf Johansson, Tuwe Löfström, Henrik Boström[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.4)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization (SDM 2019)[0m
[38;5;12m  - Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.32)[39m

[38;5;12m- [39m[38;5;14m[1mForest Packing: Fast Parallel, Decision Forests (SDM 2019)[0m
[38;5;12m  - James Browne, Disa Mhembere, Tyler M. Tomita, Joshua T. Vogelstein, Randal Burns[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.6)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mBlock-distributed Gradient Boosted Trees (SIGIR 2019)[0m
[38;5;12m  - Theodore Vasiloudis, Hyunsu Cho, Henrik Boström[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1904.10522)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mEntity Personalized Talent Search Models with Tree Interaction Features (WWW 2019)[0m
[38;5;12m  - Cagri Ozcaglar, Sahin Cem Geyik, Brian Schmitz, Prakhar Sharma, Alex Shelkovnykov, Yiming Ma, Erik Buchanan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1902.09041)[39m

[38;2;255;187;0m[4m2018[0m
[38;5;12m- [39m[38;5;14m[1mAdapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees (AAAI 2018)[0m
[38;5;12m  - Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M. Winands, Ann Nowé[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16183/16394)[39m

[38;5;12m- [39m[38;5;14m[1mMERCS: Multi-Directional Ensembles of Regression and Classification Trees (AAAI 2018)[0m
[38;5;12m  - Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16875/16735)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/eliavw/mercs-v5)[39m

[38;5;12m- [39m[38;5;14m[1mDifferential Performance Debugging With Discriminant Regression Trees (AAAI 2018)[0m
[38;5;12m  - Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Ashutosh Trivedi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1711.04076)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/cuplv/DPDEBUGGER)[39m

[38;5;12m- [39m[38;5;14m[1mEstimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction (AAAI 2018)[0m
[38;5;12m  - Jessa Bekker, Jesse Davis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16776)[39m

[38;5;12m- [39m[38;5;14m[1mMDP-Based Cost Sensitive Classification Using Decision Trees (AAAI 2018)[0m
[38;5;12m  - Shlomi Maliah, Guy Shani[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17128)[39m

[38;5;12m- [39m[38;5;14m[1mGenerative Adversarial Image Synthesis With Decision Tree Latent Controller (CVPR 2018)[0m
[38;5;12m  - Takuhiro Kaneko, Kaoru Hiramatsu, Kunio Kashino[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1805.10603)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/LynnHo/DTLC-GAN-Tensorflow)[39m

[38;5;12m- [39m[38;5;14m[1mEnhancing Very Fast Decision Trees with Local Split-Time Predictions (ICDM 2018)[0m
[38;5;12m  - Viktor Losing, Heiko Wersing, Barbara Hammer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.techfak.uni-bielefeld.de/~hwersing/LosingHammerWersing_ICDM2018.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ICDM2018Submission/VFDT-split-time-prediction)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mRealization of Random Forest for Real-Time Evaluation through Tree Framing (ICDM 2018)[0m
[38;5;12m  - Sebastian Buschjäger, Kuan-Hsun Chen, Jian-Jia Chen, Katharina Morik[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://sfb876.tu-dortmund.de/PublicPublicationFiles/buschjaeger_2018a.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFinding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)[0m
[38;5;12m  - Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.06640)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/bsharchilev/influence_boosting)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Optimal Decision Trees with SAT (IJCAI 2018)[0m
[38;5;12m  - Nina Narodytska, Alexey Ignatiev, Filipe Pereira, João Marques-Silva[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2018/0189.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mExtremely Fast Decision Tree (KDD 2018)[0m
[38;5;12m  - Chaitanya Manapragada, Geoffrey I. Webb, Mahsa Salehi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.08780)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/doubleplusplus/incremental_decision_tree-CART-Random_Forest_python)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mRapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization (KDD 2018)[0m
[38;5;12m  - Ting Ye, Hucheng Zhou, Will Y. Zou, Bin Gao, Ruofei Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://ai.stanford.edu/~wzou/kdd_rapidscorer.pdf)[39m
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[38;5;12m  - Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/7898-catboost-unbiased-boosting-with-categorical-features.pdf)[39m
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[38;5;12m- [39m[38;5;14m[1mActive Learning for Non-Parametric Regression Using Purely Random Trees (NIPS 2018)[0m
[38;5;12m  - Jack Goetz, Ambuj Tewari, Paul Zimmerman[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/7520-active-learning-for-non-parametric-regression-using-purely-random-trees.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAlternating Optimization of Decision Trees with Application to Learning Sparse Oblique Trees (NIPS 2018)[0m
[38;5;12m  - Miguel Á. Carreira-Perpiñán, Pooya Tavallali[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/7397-alternating-optimization-of-decision-trees-with-application-to-learning-sparse-oblique-trees)[39m

[38;5;12m- [39m[38;5;14m[1mMulti-Layered Gradient Boosting Decision Trees (NIPS 2018)[0m
[38;5;12m  - Ji Feng, Yang Yu, Zhi-Hua Zhou[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/7614-multi-layered-gradient-boosting-decision-trees.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/kingfengji/mGBDT)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mTransparent Tree Ensembles (SIGIR 2018)[0m
[38;5;12m  - Alexander Moore, Vanessa Murdock, Yaxiong Cai, Kristine Jones[39m
[38;5;12m  [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(http://delivery.acm.org/10.1145/3220000/3210151/p1241-moore.pdf?ip=129.215.164.203&id=3210151&acc=ACTIVE%20SERVICE&key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1559054892_a29816c683aa83a0ce0fbb7[39m
[38;5;12m77c68daba)[39m

[38;5;12m- [39m[38;5;14m[1mPrivacy-aware Ranking with Tree Ensembles on the Cloud (SIGIR 2018)[0m
[38;5;12m  - Shiyu Ji, Jinjin Shao, Daniel Agun, Tao Yang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://sites.cs.ucsb.edu/projects/ds/sigir18.pdf)[39m

[38;2;255;187;0m[4m2017[0m
[38;5;12m- [39m[38;5;14m[1mStrategic Sequences of Arguments for Persuasion Using Decision Trees (AAAI 2017)[0m
[38;5;12m  - Emmanuel Hadoux, Anthony Hunter[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/aaai17.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)[0m
[38;5;12m  - Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://melmeric.files.wordpress.com/2010/05/boostvht-boosting-distributed-streaming-decision-trees.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLatency Reduction via Decision Tree Based Query Construction (CIKM 2017)[0m
[38;5;12m  - Aman Grover, Dhruv Arya, Ganesh Venkataraman[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3132865)[39m

[38;5;12m- [39m[38;5;14m[1mEnumerating Distinct Decision Trees (ICML 2017)[0m
[38;5;12m  - Salvatore Ruggieri[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v70/ruggieri17a/ruggieri17a.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mGradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)[0m
[38;5;12m  - Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v70/si17a.html)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/springdaisy/GBDT)[39m

[38;5;12m- [39m[38;5;14m[1mConsistent Feature Attribution for Tree Ensembles (ICML 2017)[0m
[38;5;12m  - Scott M. Lundberg, Su-In Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1706.06060)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/slundberg/shap)[39m

[38;5;12m- [39m[38;5;14m[1mExtremely Fast Decision Tree Mining for Evolving Data Streams (KDD 2017)[0m
[38;5;12m  - Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://core.ac.uk/download/pdf/151040580.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)[0m
[38;5;12m  - Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1810.11363)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://catboost.ai/)[39m

[38;5;12m- [39m[38;5;14m[1mLightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)[0m
[38;5;12m  - Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://lightgbm.readthedocs.io/en/latest/)[39m

[38;5;12m- [39m[38;5;14m[1mVariable Importance Using Decision Trees (NIPS 2017)[0m
[38;5;12m  - Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/6646-variable-importance-using-decision-trees)[39m

[38;5;12m- [39m[38;5;14m[1mA Unified Approach to Interpreting Model Predictions (NIPS 2017)[0m
[38;5;12m  - Scott M. Lundberg, Su-In Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/slundberg/shap)[39m

[38;5;12m- [39m[38;5;14m[1mPruning Decision Trees via Max-Heap Projection (SDM 2017)[0m
[38;5;12m  - Zhi Nie, Binbin Lin, Shuai Huang, Naren Ramakrishnan, Wei Fan, Jieping Ye[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/317485748_Pruning_Decision_Trees_via_Max-Heap_Projection)[39m

[38;5;12m- [39m[38;5;14m[1mA Practical Method for Solving Contextual Bandit Problems Using Decision Trees (UAI 2017)[0m
[38;5;12m  - Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1706.04687)[39m

[38;5;12m- [39m[38;5;14m[1mComplexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility (UAI 2017)[0m
[38;5;12m  - Hugo Gilbert, Olivier Spanjaard[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://auai.org/uai2017/proceedings/papers/64.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mGB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)[0m
[38;5;12m  - Qian Zhao, Yue Shi, Liangjie Hong[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1311.pdf)[39m

[38;2;255;187;0m[4m2016[0m
[38;5;12m- [39m[38;5;14m[1mSparse Perceptron Decision Tree for Millions of Dimensions (AAAI 2016)[0m
[38;5;12m  - Weiwei Liu, Ivor W. Tsang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12111)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees (CVPR 2016)[0m
[38;5;12m  - Jianhui Chen, Hoang Minh Le, Peter Carr, Yisong Yue, James J. Little[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://hoangle.info/papers/cvpr2016_online_smooth_long.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOnline Learning with Bayesian Classification Trees (CVPR 2016)[0m
[38;5;12m  - Samuel Rota Bulò, Peter Kontschieder[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.dsi.unive.it/~srotabul/files/publications/CVPR2016.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAccurate Robust and Efficient Error Estimation for Decision Trees (ICML 2016)[0m
[38;5;12m  - Lixin Fan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v48/fan16.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mMeta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)[0m
[38;5;12m  - Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v48/ustinovskiy16.html)[39m

[38;5;12m- [39m[38;5;14m[1mBoosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)[0m
[38;5;12m  - Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.kdd.org/kdd2016/papers/files/adf0653-poyarkovA.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mXGBoost: A Scalable Tree Boosting System (KDD 2016)[0m
[38;5;12m  - Tianqi Chen, Carlos Guestrin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://xgboost.readthedocs.io/en/latest/)[39m

[38;5;12m- [39m[38;5;14m[1mYggdrasil: An Optimized System for Training Deep Decision Trees at Scale (NIPS 2016)[0m
[38;5;12m  - Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/6366-yggdrasil-an-optimized-system-for-training-deep-decision-trees-at-scale)[39m

[38;5;12m- [39m[38;5;14m[1mA Communication-Efficient Parallel Algorithm for Decision Tree (NIPS 2016)[0m
[38;5;12m  - Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1611.01276)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst)[39m

[38;5;12m- [39m[38;5;14m[1mExploiting CPU SIMD Extensions to Speed-up Document Scoring with Tree Ensembles (SIGIR 2016)[0m
[38;5;12m  - Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://pages.di.unipi.it/rossano/wp-content/uploads/sites/7/2016/07/SIGIR16a.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/hpclab/vectorized-quickscorer)[39m

[38;5;12m- [39m[38;5;14m[1mPost-Learning Optimization of Tree Ensembles for Efficient Ranking (SIGIR 2016)[0m
[38;5;12m  - Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, Salvatore Trani[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/305081572_Post-Learning_Optimization_of_Tree_Ensembles_for_Efficient_Ranking)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/hpclab/quickrank)[39m

[38;2;255;187;0m[4m2015[0m
[38;5;12m- [39m[38;5;14m[1mParticle Gibbs for Bayesian Additive Regression Trees (AISTATS 2015)[0m
[38;5;12m  - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1502.04622)[39m

[38;5;12m- [39m[38;5;14m[1mDART: Dropouts Meet Multiple Additive Regression Trees (AISTATS 2015)[0m
[38;5;12m  - Korlakai Vinayak Rashmi, Ran Gilad-Bachrach[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1505.01866)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://xgboost.readthedocs.io/en/latest/)[39m

[38;5;12m- [39m[38;5;14m[1mSingle Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-level Appearance Models (CVPR 2015)[0m
[38;5;12m  - Jingjing Xiao, Rustam Stolkin, Ales Leonardis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_058.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFace Alignment Using Cascade Gaussian Process Regression Trees (CVPR 2015)[0m
[38;5;12m  - Donghoon Lee, Hyunsin Park, Chang Dong Yoo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://slsp.kaist.ac.kr/paperdata/Face_Alignment_Using.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/donghoonlee04/cGPRT)[39m

[38;5;12m- [39m[38;5;14m[1mTracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)[0m
[38;5;12m  - Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Son_Tracking-by-Segmentation_With_Online_ICCV_2015_paper.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mEntropy Evaluation Based on Confidence Intervals of Frequency Estimates : Application to the Learning of Decision Trees (ICML 2015)[0m
[38;5;12m  - Mathieu Serrurier, Henri Prade[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v37/serrurier15.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mLarge-scale Distributed Dependent Nonparametric Trees (ICML 2015)[0m
[38;5;12m  - Zhiting Hu, Qirong Ho, Avinava Dubey, Eric P. Xing[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.cmu.edu/~zhitingh/data/icml15hu.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOptimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)[0m
[38;5;12m  - Zhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cse.wustl.edu/~ychen/public/OAE.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Decision Tree Framework for Spatiotemporal Sequence Prediction (KDD 2015)[0m
[38;5;12m  - Taehwan Kim, Yisong Yue, Sarah L. Taylor, Iain A. Matthews[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.yisongyue.com/publications/kdd2015_ssw_dt.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Non-greedy Optimization of Decision Trees (NIPS 2015)[0m
[38;5;12m  - Mohammad Norouzi, Maxwell D. Collins, Matthew Johnson, David J. Fleet, Pushmeet Kohli[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1511.04056)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mQuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees (SIGIR 2015)[0m
[38;5;12m  - Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://pages.di.unipi.it/rossano/wp-content/uploads/sites/7/2015/11/sigir15.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/hpclab/quickrank)[39m

[38;2;255;187;0m[4m2014[0m

[38;5;12m- [39m[38;5;14m[1mA Mixtures-of-Trees Framework for Multi-Label Classification (CIKM 2014)[0m
[38;5;12m  - Charmgil Hong, Iyad Batal, Milos Hauskrecht[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410801/)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mOn Building Decision Trees from Large-scale Data in Applications of On-line Advertising (CIKM 2014)[0m
[38;5;12m  - Shivaram Kalyanakrishnan, Deepthi Singh, Ravi Kant[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cse.iitb.ac.in/~shivaram/papers/ksk_cikm_2014.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFast Supervised Hashing with Decision Trees for High-Dimensional Data (CVPR 2014)[0m
[38;5;12m  - Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1404.1561)[39m

[38;5;12m- [39m[38;5;14m[1mOne Millisecond Face Alignment with an Ensemble of Regression Trees (CVPR 2014)[0m
[38;5;12m  - Vahid Kazemi, Josephine Sullivan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/264419855_One_Millisecond_Face_Alignment_with_an_Ensemble_of_Regression_Trees)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mThe return of AdaBoost.MH: multi-class Hamming trees (ICLR 2014)[0m
[38;5;12m  - Balázs Kégl[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1312.6086.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDiagnosis Determination: Decision Trees Optimizing Simultaneously Worst and Expected Testing Cost (ICML 2014)[0m
[38;5;12m  - Ferdinando Cicalese, Eduardo Sany Laber, Aline Medeiros Saettler[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/47ae/852f83b76f95b27ab00308d04f6020bdf71f.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mLearning Multiple-Question Decision Trees for Cold-Start Recommendation (WSDM 2013)[0m
[38;5;12m  - Mingxuan Sun, Fuxin Li, Joonseok Lee, Ke Zhou, Guy Lebanon, Hongyuan Zha[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.joonseok.net/papers/coldstart.pdf)[39m

[38;2;255;187;0m[4m2013[0m
[38;5;12m- [39m[38;5;14m[1mWeakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria (ICCV 2013)[0m
[38;5;12m  - Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/6751340)[39m

[38;5;12m- [39m[38;5;14m[1mRevisiting Example Dependent Cost-Sensitive Learning with Decision Trees (ICCV 2013)[0m
[38;5;12m  - Oisin Mac Aodha, Gabriel J. Brostow[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/6751133)[39m

[38;5;12m- [39m[38;5;14m[1mConformal Prediction Using Decision Trees (ICDM 2013)[0m
[38;5;12m  - Ulf Johansson, Henrik Boström, Tuve Löfström[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/abstract/document/6729517)[39m

[38;5;12m- [39m[38;5;14m[1mFocal-Test-Based Spatial Decision Tree Learning: A Summary of Results (ICDM 2013)[0m
[38;5;12m  - Zhe Jiang, Shashi Shekhar, Xun Zhou, Joseph K. Knight, Jennifer Corcoran[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/f28e/df8d9eed76e4ce97cb6bd4182d590547be5e.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mTop-down Particle Filtering for Bayesian Decision Trees (ICML 2013)[0m
[38;5;12m  - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1303.0561)[39m

[38;5;12m- [39m[38;5;14m[1mQuickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)[0m
[38;5;12m  - Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v28/appel13.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mKnowledge Compilation for Model Counting: Affine Decision Trees (IJCAI 2013)[0m
[38;5;12m  - Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, Samuel Thomas[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/262398921_Knowledge_Compilation_for_Model_Counting_Affine_Decision_Trees)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mUnderstanding Variable Importances in Forests of Randomized Trees (NIPS 2013)[0m
[38;5;12m  - Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/4928-understanding-variable-importances-in-forests-of-randomized-trees)[39m

[38;5;12m- [39m[38;5;14m[1mRegression-tree Tuning in a Streaming Setting (NIPS 2013)[0m
[38;5;12m  - Samory Kpotufe, Francesco Orabona[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/4898-regression-tree-tuning-in-a-streaming-setting)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Max-Margin Tree Predictors (UAI 2013)[0m
[38;5;12m  - Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ttic.uchicago.edu/~meshi/papers/mtreen.pdf)[39m

[38;2;255;187;0m[4m2012[0m
[38;5;12m- [39m[38;5;14m[1mRegression Tree Fields - An Efficient, Non-parametric Approach to Image Labeling Problems (CVPR 2012)[0m
[38;5;12m  - Jeremy Jancsary, Sebastian Nowozin, Toby Sharp, Carsten Rother[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.nowozin.net/sebastian/papers/jancsary2012rtf.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mConfDTree: Improving Decision Trees Using Confidence Intervals (ICDM 2012)[0m
[38;5;12m  - Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/6413889)[39m

[38;5;12m- [39m[38;5;14m[1mImproved Information Gain Estimates for Decision Tree Induction (ICML 2012)[0m
[38;5;12m  - Sebastian Nowozin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1206.4620)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Partially Observable Models Using Temporally Abstract Decision Trees (NIPS 2012)[0m
[38;5;12m  - Erik Talvitie[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/4662-learning-partially-observable-models-using-temporally-abstract-decision-trees)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mSubtree Replacement in Decision Tree Simplification (SDM 2012)[0m
[38;5;12m  - Salvatore Ruggieri[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://pages.di.unipi.it/ruggieri/Papers/sdm2012.pdf)[39m

[38;2;255;187;0m[4m2011[0m
[38;5;12m- [39m[38;5;14m[1mIncorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)[0m
[38;5;12m  - Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/viewFile/3711/4086)[39m

[38;5;12m- [39m[38;5;14m[1mSyntactic Decision Tree LMs: Random Selection or Intelligent Design (EMNLP 2011)[0m
[38;5;12m  - Denis Filimonov, Mary P. Harper[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/D11-1064)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mDecision Tree Fields (ICCV 2011)[0m
[38;5;12m  - Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, Pushmeet Kohli[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/nrbsyk_iccv11.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mConfidence in Predictions from Random Tree Ensembles (ICDM 2011)[0m
[38;5;12m  - Siddhartha Bhattacharyya[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/article/10.1007/s10115-012-0600-z)[39m

[38;5;12m- [39m[38;5;14m[1mSpeeding-Up Hoeffding-Based Regression Trees With Options (ICML 2011)[0m
[38;5;12m  - Elena Ikonomovska, João Gama, Bernard Zenko, Saso Dzeroski[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://icml.cc/Conferences/2011/papers/349_icmlpaper.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mDensity Estimation Trees (KDD 2011)[0m
[38;5;12m  - Parikshit Ram, Alexander G. Gray[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://mlpack.org/papers/det.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mBagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)[0m
[38;5;12m  - Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/materials/bagging_lmbamart_jforests.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOn the Complexity of Decision Making in Possibilistic Decision Trees (UAI 2011)[0m
[38;5;12m  - Hélène Fargier, Nahla Ben Amor, Wided Guezguez[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dslpitt.org/uai/papers/11/p203-fargier.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAdaptive Bootstrapping of Recommender Systems Using Decision Trees (WSDM 2011)[0m
[38;5;12m  - Nadav Golbandi, Yehuda Koren, Ronny Lempel[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=1935910)[39m

[38;5;12m- [39m[38;5;14m[1mParallel Boosted Regression Trees for Web Search Ranking (WWW 2011)[0m
[38;5;12m  - Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.cornell.edu/~kilian/papers/fr819-tyreeA.pdf)[39m

[38;2;255;187;0m[4m2010[0m
[38;5;12m- [39m[38;5;14m[1mDiscrimination Aware Decision Tree Learning (ICDM 2010)[0m
[38;5;12m  - Faisal Kamiran, Toon Calders, Mykola Pechenizkiy[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.win.tue.nl/~mpechen/publications/pubs/KamiranICDM2010.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Trees for Uplift Modeling (ICDM 2010)[0m
[38;5;12m  - Piotr Rzepakowski, Szymon Jaroszewicz[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://core.ac.uk/download/pdf/81899141.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Markov Network Structure with Decision Trees (ICDM 2010)[0m
[38;5;12m  - Daniel Lowd, Jesse Davis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ix.cs.uoregon.edu/~lowd/icdm10lowd.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mMultivariate Dyadic Regression Trees for Sparse Learning Problems (NIPS 2010)[0m
[38;5;12m  - Han Liu, Xi Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/4178-multivariate-dyadic-regression-trees-for-sparse-learning-problems.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFast and Accurate Gene Prediction by Decision Tree Classification (SDM 2010)[0m
[38;5;12m  - Rong She, Jeffrey Shih-Chieh Chu, Ke Wang, Nansheng Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.sfu.ca/~chenn/genBlastDT_sdm.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Robust Decision Tree Algorithm for Imbalanced Data Sets (SDM 2010)[0m
[38;5;12m  - Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www3.nd.edu/~nchawla/papers/SDM10.pdf)[39m

[38;2;255;187;0m[4m2009[0m
[38;5;12m- [39m[38;5;14m[1mStochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)[0m
[38;5;12m  - Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=1646301)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFeature Selection for Ranking Using Boosted Trees (CIKM 2009)[0m
[38;5;12m  - Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.francosalvetti.com/cikm09_camera2.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mThai Word Segmentation with Hidden Markov Model and Decision Tree (PAKDD 2009)[0m
[38;5;12m  - Poramin Bheganan, Richi Nayak, Yue Xu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-642-01307-2_10)[39m

[38;5;12m- [39m[38;5;14m[1mParameter Estimdation in Semi-Random Decision Tree Ensembling on Streaming Data (PAKDD 2009)[0m
[38;5;12m  - Pei-Pei Li, Qianhui Liang, Xindong Wu, Xuegang Hu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-642-01307-2_35)[39m

[38;5;12m- [39m[38;5;14m[1mDTU: A Decision Tree for Uncertain Data (PAKDD 2009)[0m
[38;5;12m  - Biao Qin, Yuni Xia, Fang Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-642-01307-2_4)[39m

[38;2;255;187;0m[4m2008[0m
[38;5;12m- [39m[38;5;14m[1mPredicting Future Decision Trees from Evolving Data (ICDM 2008)[0m
[38;5;12m  - Mirko Böttcher, Martin Spott, Rudolf Kruse[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/4781098)[39m

[38;5;12m- [39m[38;5;14m[1mBayes Optimal Classification for Decision Trees (ICML 2008)[0m
[38;5;12m  - Siegfried Nijssen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://icml2008.cs.helsinki.fi/papers/455.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA New Credit Scoring Method Based on Rough Sets and Decision Tree (PAKDD 2008)[0m
[38;5;12m  - XiYue Zhou, Defu Zhang, Yi Jiang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-540-68125-0_117)[39m

[38;5;12m- [39m[38;5;14m[1mA Comparison of Different Off-Centered Entropies to Deal with Class Imbalance for Decision Trees (PAKDD 2008)[0m
[38;5;12m  - Philippe Lenca, Stéphane Lallich, Thanh-Nghi Do, Nguyen-Khang Pham[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-540-68125-0_59)[39m

[38;5;12m- [39m[38;5;14m[1mBOAI: Fast Alternating Decision Tree Induction Based on Bottom-Up Evaluation (PAKDD 2008)[0m
[38;5;12m  - Bishan Yang, Tengjiao Wang, Dongqing Yang, Lei Chang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-540-68125-0_36)[39m

[38;5;12m- [39m[38;5;14m[1mA General Framework for Estimating Similarity of Datasets and Decision Trees: Exploring Semantic Similarity of Decision Trees (SDM 2008)[0m
[38;5;12m  - Irene Ntoutsi, Alexandros Kalousis, Yannis Theodoridis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/220907047_A_general_framework_for_estimating_similarity_of_datasets_and_decision_trees_exploring_semantic_similarity_of_decision_trees)[39m

[38;5;12m- [39m[38;5;14m[1mROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data (SDM 2008)[0m
[38;5;12m  - M. Maruf Hossain, Md. Rafiul Hassan, James Bailey[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/bd80/db2f0903169b7611d34b2cc85f60a736375d.pdf)[39m

[38;2;255;187;0m[4m2007[0m

[38;5;12m- [39m[38;5;14m[1mTree-based Classifiers for Bilayer Video Segmentation (CVPR 2007)[0m
[38;5;12m  - Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/4270033)[39m

[38;5;12m- [39m[38;5;14m[1mAdditive Groves of Regression Trees (ECML 2007)[0m
[38;5;12m  - Daria Sorokina, Rich Caruana, Mirek Riedewald[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://additivegroves.net/papers/groves.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Tree Instability and Active Learning (ECML 2007)[0m
[38;5;12m  - Kenneth Dwyer, Robert Holte[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://webdocs.cs.ualberta.ca/~holte/Publications/ecml07.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mEnsembles of Multi-Objective Decision Trees (ECML 2007)[0m
[38;5;12m  - Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-540-74958-5_61)[39m

[38;5;12m- [39m[38;5;14m[1mSeeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble (ECML 2007)[0m
[38;5;12m  - Anneleen Van Assche, Hendrik Blockeel[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://ftp.cs.wisc.edu/machine-learning/shavlik-group/ilp07wip/ilp07_assche.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSample Compression Bounds for Decision Trees (ICML 2007)[0m
[38;5;12m  - Mohak Shah[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.9136&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Tighter Error Bound for Decision Tree Learning Using PAC Learnability (IJCAI 2007)[0m
[38;5;12m  - Chaithanya Pichuka, Raju S. Bapi, Chakravarthy Bhagvati, Arun K. Pujari, Bulusu Lakshmana Deekshatulu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/07/Papers/163.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mKeep the Decision Tree and Estimate the Class Probabilities Using its Decision Boundary (IJCAI 2007)[0m
[38;5;12m  - Isabelle Alvarez, Stephan Bernard, Guillaume Deffuant[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/07/Papers/104.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mReal Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)[0m
[38;5;12m  - Claudia Henry, Richard Nock, Frank Nielsen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/07/Papers/135.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mScalable Look-ahead Linear Regression Trees (KDD 2007)[0m
[38;5;12m  - David S. Vogel, Ognian Asparouhov, Tobias Scheffer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.uni-potsdam.de/ml/publications/kdd2007.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mMining Optimal Decision Trees from Itemset Lattices (KDD 2007)[0m
[38;5;12m  - Siegfried Nijssen, Élisa Fromont[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://hal.archives-ouvertes.fr/hal-00372011/document)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Hybrid Multi-group Privacy-Preserving Approach for Building Decision Trees (PAKDD 2007)[0m
[38;5;12m  - Zhouxuan Teng, Wenliang Du[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-540-71701-0_30)[39m

[38;2;255;187;0m[4m2006[0m
[38;5;12m- [39m[38;5;14m[1mDecision Tree Methods for Finding Reusable MDP Homomorphisms (AAAI 2006)[0m
[38;5;12m  - Alicia P. Wolfe, Andrew G. Barto[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/AAAI/2006/AAAI06-085.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Fast Decision Tree Learning Algorithm (AAAI 2006)[0m
[38;5;12m  - Jiang Su, Harry Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.unb.ca/~hzhang/publications/AAAI06.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAnytime Induction of Decision Trees: An Iterative Improvement Approach (AAAI 2006)[0m
[38;5;12m  - Saher Esmeir, Shaul Markovitch[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/AAAI/2006/AAAI06-056.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mWhen a Decision Tree Learner Has Plenty of Time (AAAI 2006)[0m
[38;5;12m  - Saher Esmeir, Shaul Markovitch[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/AAAI/2006/AAAI06-259.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Trees for Functional Variables (ICDM 2006)[0m
[38;5;12m  - Suhrid Balakrishnan, David Madigan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://archive.dimacs.rutgers.edu/Research/MMS/PAPERS/fdt17.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCost-Sensitive Decision Tree Learning for Forensic Classification (ECML 2006)[0m
[38;5;12m  - Jason V. Davis, Jungwoo Ha, Christopher J. Rossbach, Hany E. Ramadan, Emmett Witchel[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.utexas.edu/users/witchel/pubs/davis-ecml06.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mImproving the Ranking Performance of Decision Trees (ECML 2006)[0m
[38;5;12m  - Bin Wang, Harry Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/11871842_44)[39m

[38;5;12m- [39m[38;5;14m[1mA General Framework for Accurate and Fast Regression by Data Summarization in Random Decision Trees (KDD 2006)[0m
[38;5;12m  - Wei Fan, Joe McCloskey, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.442.2004&rep=rep1&type=pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mConstructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction (PAKDD 2006)[0m
[38;5;12m  - Phu Chien Nguyen, Kouzou Ohara, Akira Mogi, Hiroshi Motoda, Takashi Washio[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.ar.sanken.osaka-u.ac.jp/~motoda/papers/pakdd06.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mVariable Randomness in Decision Tree Ensembles (PAKDD 2006)[0m
[38;5;12m  - Fei Tony Liu, Kai Ming Ting[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/11731139_12)[39m

[38;5;12m- [39m[38;5;14m[1mGeneralized Conditional Entropy and a Metric Splitting Criterion for Decision Trees (PAKDD 2006)[0m
[38;5;12m  - Dan A. Simovici, Szymon Jaroszewicz[39m
[38;5;12m  [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(https://www.researchgate.net/profile/Szymon_Jaroszewicz/publication/220895184_Generalized_Conditional_Entropy_and_a_Metric_Splitting_Criterion_for_Decision_Trees/links/0fcfd50b1267f7b868000000/Generalized-Conditional-Entropy-and-a-Metr[39m
[38;5;12mic-Splitting-Criterion-for-Decision-Trees.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics (PKDD 2006)[0m
[38;5;12m  - Hendrik Blockeel, Leander Schietgat, Jan Struyf, Saso Dzeroski, Amanda Clare[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/11871637_7)[39m

[38;5;12m- [39m[38;5;14m[1mk-Anonymous Decision Tree Induction (PKDD 2006)[0m
[38;5;12m  - Arik Friedman, Assaf Schuster, Ran Wolff[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.technion.ac.il/~arikf/online-publications/kADET06.pdf)[39m

[38;2;255;187;0m[4m2005[0m
[38;5;12m- [39m[38;5;14m[1mRepresenting Conditional Independence Using Decision Trees (AAAI 2005)[0m
[38;5;12m  - Jiang Su, Harry Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.unb.ca/~hzhang/publications/AAAI051SuJ.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUse of Expert Knowledge for Decision Tree Pruning (AAAI 2005)[0m
[38;5;12m  - Jingfeng Cai, John Durkin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.aaai.org/Papers/AAAI/2005/SA05-009.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mModel Selection in Omnivariate Decision Trees (ECML 2005)[0m
[38;5;12m  - Olcay Taner Yildiz, Ethem Alpaydin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cmpe.boun.edu.tr/~ethem/files/papers/yildiz_ecml05.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCombining Bias and Variance Reduction Techniques for Regression Trees (ECML 2005)[0m
[38;5;12m  - Yuk Lai Suen, Prem Melville, Raymond J. Mooney[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.utexas.edu/users/ml/papers/bv-ecml-05.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSimple Test Strategies for Cost-Sensitive Decision Trees (ECML 2005)[0m
[38;5;12m  - Shengli Sheng, Charles X. Ling, Qiang Yang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/3297582_Test_strategies_for_cost-sensitive_decision_trees)[39m

[38;5;12m- [39m[38;5;14m[1mEffective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches (ICDM 2005)[0m
[38;5;12m  - Wei Fan, Ed Greengrass, Joe McCloskey, Philip S. Yu, Kevin Drummey[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.9713&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mExploiting Informative Priors for Bayesian Classification and Regression Trees (IJCAI 2005)[0m
[38;5;12m  - Nicos Angelopoulos, James Cussens[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/05/Papers/1013.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mRanking Cases with Decision Trees: a Geometric Method that Preserves Intelligibility (IJCAI 2005)[0m
[38;5;12m  - Isabelle Alvarez, Stephan Bernard[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/05/Papers/1502.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mMaximizing Tree Diversity by Building Complete-Random Decision Trees (PAKDD 2005)[0m
[38;5;12m  - Fei Tony Liu, Kai Ming Ting, Wei Fan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.7805&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mHybrid Cost-Sensitive Decision Tree (PKDD 2005)[0m
[38;5;12m  - Shengli Sheng, Charles X. Ling[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://cling.csd.uwo.ca/papers/pkdd05a.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mTree2 - Decision Trees for Tree Structured Data (PKDD 2005)[0m
[38;5;12m  - Björn Bringmann, Albrecht Zimmermann[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/11564126_10)[39m

[38;5;12m- [39m[38;5;14m[1mBuilding Decision Trees on Records Linked through Key References (SDM 2005)[0m
[38;5;12m  - Ke Wang, Yabo Xu, Philip S. Yu, Rong She[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.7181&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Tree Induction in High Dimensional, Hierarchically Distributed Databases (SDM 2005)[0m
[38;5;12m  - Amir Bar-Or, Ran Wolff, Assaf Schuster, Daniel Keren[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.semanticscholar.org/paper/Decision-Tree-Induction-in-High-Dimensional%2C-Bar-Or-Wolff/90235fc35c27dae273681f7847c2b20ff37928a9)[39m

[38;5;12m- [39m[38;5;14m[1mBoosted Decision Trees for Word Recognition in Handwritten Document Retrieval (SIGIR 2005)[0m
[38;5;12m  - Nicholas R. Howe, Toni M. Rath, R. Manmatha[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.1551&rep=rep1&type=pdf)[39m

[38;2;255;187;0m[4m2004[0m
[38;5;12m- [39m[38;5;14m[1mOn the Optimality of Probability Estimation by Random Decision Trees (AAAI 2004)[0m
[38;5;12m  - Wei Fan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.447.2128&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOccam's Razor and a Non-Syntactic Measure of Decision Tree Complexity (AAAI 2004)[0m
[38;5;12m  - Goutam Paul[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/AAAI/2004/AAAI04-130.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUsing Emerging Patterns and Decision Trees in Rare-Class Classification (ICDM 2004)[0m
[38;5;12m  - Hamad Alhammady, Kotagiri Ramamohanarao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/abstract/document/1410299)[39m

[38;5;12m- [39m[38;5;14m[1mOrthogonal Decision Trees (ICDM 2004)[0m
[38;5;12m  - Hillol Kargupta, Haimonti Dutta[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.csee.umbc.edu/~hillol/PUBS/odtree.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mImproving the Reliability of Decision Tree and Naive Bayes Learners (ICDM 2004)[0m
[38;5;12m  - David George Lindsay, Siân Cox[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.521.3127&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCommunication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data (ICDM 2004)[0m
[38;5;12m  - Chris Giannella, Kun Liu, Todd Olsen, Hillol Kargupta[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.7119&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams (ICDM 2004)[0m
[38;5;12m  - Wei Fan, Yi-an Huang, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.9450&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLookahead-based Algorithms for Anytime Induction of Decision Trees (ICML 2004)[0m
[38;5;12m  - Saher Esmeir, Shaul Markovitch[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.technion.ac.il/~shaulm/papers/pdf/Esmeir-Markovitch-icml2004.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mDecision Trees with Minimal Costs (ICML 2004)[0m
[38;5;12m  - Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://icml.cc/Conferences/2004/proceedings/papers/136.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mTraining Conditional Random Fields via Gradient Tree Boosting (ICML 2004)[0m
[38;5;12m  - Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://web.engr.oregonstate.edu/~tgd/publications/ml2004-treecrf.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDetecting Structural Metadata with Decision Trees and Transformation-Based Learning (NAACL 2004)[0m
[38;5;12m  - Joungbum Kim, Sarah E. Schwarm, Mari Ostendorf[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/N04-1018)[39m

[38;5;12m- [39m[38;5;14m[1mOn the Adaptive Properties of Decision Trees (NIPS 2004)[0m
[38;5;12m  - Clayton D. Scott, Robert D. Nowak[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/2625-on-the-adaptive-properties-of-decision-trees.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index (PAKDD 2004)[0m
[38;5;12m  - Dan A. Simovici, Szymon Jaroszewicz[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/2906289_A_Metric_Approach_to_Building_Decision_Trees_Based_on_Goodman-Kruskal_Association_Index)[39m

[38;2;255;187;0m[4m2003[0m
[38;5;12m- [39m[38;5;14m[1mRademacher Penalization over Decision Tree Prunings (ECML 2003)[0m
[38;5;12m  - Matti Kääriäinen, Tapio Elomaa[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/221112653_Rademacher_Penalization_over_Decision_Tree_Prunings)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mEnsembles of Cascading Trees (ICDM 2003)[0m
[38;5;12m  - Jinyan Li, Huiqing Liu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/4047523_Ensembles_of_cascading_trees)[39m

[38;5;12m- [39m[38;5;14m[1mPostprocessing Decision Trees to Extract Actionable Knowledge (ICDM 2003)[0m
[38;5;12m  - Qiang Yang, Jie Yin, Charles X. Ling, Tielin Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/b2c6/ff54c7aeefc70820ff04a8fc8b804012c504.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mK-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier (ICDM 2003)[0m
[38;5;12m  - Tomoyuki Shibata, Takekazu Kato, Toshikazu Wada[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/abstract/document/1250997)[39m

[38;5;12m- [39m[38;5;14m[1mIdentifying Markov Blankets with Decision Tree Induction (ICDM 2003)[0m
[38;5;12m  - Lewis J. Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.semanticscholar.org/paper/Identifying-Markov-Blankets-with-Decision-Tree-Frey-Fisher/1aa0b0ede22f3963c923ea320a8bed91ac5aafbf)[39m

[38;5;12m- [39m[38;5;14m[1mComparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy (ICDM 2003)[0m
[38;5;12m  - Jin Huang, Jingjing Lu, Charles X. Ling[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/8a73/74b98a9d94b8c01e996e72340f86a4327869.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBoosting Lazy Decision Trees (ICML 2003)[0m
[38;5;12m  - Xiaoli Zhang Fern, Carla E. Brodley[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/ICML/2003/ICML03-026.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Tree with Better Ranking (ICML 2003)[0m
[38;5;12m  - Charles X. Ling, Robert J. Yan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/ICML/2003/ICML03-064.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSkewing: An Efficient Alternative to Lookahead for Decision Tree Induction (IJCAI 2003)[0m
[38;5;12m  - David Page, Soumya Ray[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://pages.cs.wisc.edu/~dpage/ijcai3.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Decision Tree Construction on Streaming Data (KDD 2003)[0m
[38;5;12m  - Ruoming Jin, Gagan Agrawal[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://web.cse.ohio-state.edu/~agrawal.28/p/sigkdd03.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mPaintingClass: Interactive Construction Visualization and Exploration of Decision Trees (KDD 2003)[0m
[38;5;12m  - Soon Tee Teoh, Kwan-Liu Ma[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/220272011_PaintingClass_interactive_construction_visualization_and_exploration_of_decision_trees)[39m

[38;5;12m- [39m[38;5;14m[1mAccurate Decision Trees for Mining High-Speed Data Streams (KDD 2003)[0m
[38;5;12m  - João Gama, Ricardo Rocha, Pedro Medas[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://staff.icar.cnr.it/manco/Teaching/2006/datamining/Esami2006/ArticoliSelezionatiDM/SEMINARI/Mining%20Data%20Streams/kdd03.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mNear-Minimax Optimal Classification with Dyadic Classification Trees (NIPS 2003)[0m
[38;5;12m  - Clayton D. Scott, Robert D. Nowak[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://nowak.ece.wisc.edu/nips03.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mImproving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule (PAKDD 2003)[0m
[38;5;12m  - Chenzhou Ye, Jie Yang, Lixiu Yao, Nian-yi Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/220895462_Improving_Performance_of_Decision_Tree_Algorithms_with_Multi-edited_Nearest_Neighbor_Rule)[39m

[38;5;12m- [39m[38;5;14m[1mArbogodai: a New Approach for Decision Trees (PKDD 2003)[0m
[38;5;12m  - Djamel A. Zighed, Gilbert Ritschard, Walid Erray, Vasile-Marian Scuturici[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://mephisto.unige.ch/pub/publications/gr/zig_rit_arbo_pkdd03.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCommunication and Memory Efficient Parallel Decision Tree Construction (SDM 2003)[0m
[38;5;12m  - Ruoming Jin, Gagan Agrawal[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.3059&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Tree Classification of Spatial Data Patterns from Videokeratography using Zernicke Polynomials (SDM 2003)[0m
[38;5;12m  - Michael D. Twa, Srinivasan Parthasarathy, Thomas W. Raasch, Mark Bullimore[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/220907147_Decision_Tree_Classification_of_Spatial_Data_Patterns_From_Videokeratography_Using_Zernike_Polynomials)[39m

[38;2;255;187;0m[4m2002[0m

[38;5;12m- [39m[38;5;14m[1mMulticlass Alternating Decision Trees (ECML 2002)[0m
[38;5;12m  - Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark A. Hall[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.waikato.ac.nz/~bernhard/papers/ecml2002.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mHeterogeneous Forests of Decision Trees (ICANN 2002)[0m
[38;5;12m  - Krzysztof Grabczewski, Wlodzislaw Duch[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://fizyka.umk.pl/publications/kmk/02forest.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSolving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)[0m
[38;5;12m  - Jinyan Li, Limsoon Wong[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/1184021)[39m

[38;5;12m- [39m[38;5;14m[1mSolving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)[0m
[38;5;12m  - Jinyan Li, Limsoon Wong[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.comp.nus.edu.sg/~wongls/psZ/decisionTreeandEP-2.ps)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Decision Trees Using the Area Under the ROC Curve (ICML 2002)[0m
[38;5;12m  - César Ferri, Peter A. Flach, José Hernández-Orallo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://dmip.webs.upv.es/papers/ICML2002.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFinding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction (ICML 2002)[0m
[38;5;12m  - Fumio Takechi, Einoshin Suzuki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/221346121_Finding_an_Optimal_Gain-Ratio_Subset-Split_Test_for_a_Set-Valued_Attribute_in_Decision_Tree_Induction)[39m

[38;5;12m- [39m[38;5;14m[1mEfficiently Mining Frequent Trees in a Forest (KDD 2002)[0m
[38;5;12m  - Mohammed Javeed Zaki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.8511&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSECRET: a Scalable Linear Regression Tree Algorithm (KDD 2002)[0m
[38;5;12m  - Alin Dobra, Johannes Gehrke[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.cornell.edu/people/dobra/papers/secret-extended.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mInstability of Decision Tree Classification Algorithms (KDD 2002)[0m
[38;5;12m  - Ruey-Hsia Li, Geneva G. Belford[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.8094&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mExtracting Decision Trees From Trained Neural Networks (KDD 2002)[0m
[38;5;12m  - Olcay Boz[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://dspace.library.iitb.ac.in/jspui/bitstream/10054/1285/1/5664.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDyadic Classification Trees via Structural Risk Minimization (NIPS 2002)[0m
[38;5;12m  - Clayton D. Scott, Robert D. Nowak[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/2198-dyadic-classification-trees-via-structural-risk-minimization.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mApproximate Splitting for Ensembles of Trees using Histograms (SDM 2002)[0m
[38;5;12m  - Chandrika Kamath, Erick Cantú-Paz, David Littau[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/0855/0a94993a268e4e3e99c41e7e0ee43eabd993.pdf)[39m

[38;2;255;187;0m[4m2001[0m
[38;5;12m- [39m[38;5;14m[1mJapanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning (ACL 2001)[0m
[38;5;12m  - Hideki Isozaki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/P01-1041)[39m

[38;5;12m- [39m[38;5;14m[1mMessage Length as an Effective Ockham's Razor in Decision Tree Induction (AISTATS 2001)[0m
[38;5;12m  - Scott Needham, David L. Dowe[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (www.gatsby.ucl.ac.uk/aistats/aistats2001/files/needham122.ps)[39m

[38;5;12m- [39m[38;5;14m[1mSQL Database Primitives for Decision Tree Classifiers (CIKM 2001)[0m
[38;5;12m  - Kai-Uwe Sattler, Oliver Dunemann[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://fusion.cs.uni-magdeburg.de/pubs/classprim.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Unified Framework for Evaluation Metrics in Classification Using Decision Trees (ECML 2001)[0m
[38;5;12m  - Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://scholar.harvard.edu/files/nkc/files/2015_framework_for_benefit_risk_assessment_value_in_health.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBackpropagation in Decision Trees for Regression (ECML 2001)[0m
[38;5;12m  - Victor Medina-Chico, Alberto Suárez, James F. Lutsko[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/3-540-44795-4_30)[39m

[38;5;12m- [39m[38;5;14m[1mConsensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction (ECML 2001)[0m
[38;5;12m  - Branko Kavsek, Nada Lavrac, Anuska Ferligoj[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/content/pdf/10.1007/3-540-44795-4_22.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mMining Decision Trees from Data Streams in a Mobile Environment (ICDM 2001)[0m
[38;5;12m  - Hillol Kargupta, Byung-Hoon Park[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/989530)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Determination of Dynamic Split Points in a Decision Tree (ICDM 2001)[0m
[38;5;12m  - David Maxwell Chickering, Christopher Meek, Robert Rounthwaite[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/3587/a245c34ea415b205a903bde3220eb533d1a7.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)[0m
[38;5;12m  - Bernard Zenko, Ljupco Todorovski, Saso Dzeroski[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.3118&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Algorithms for Decision Tree Cross-Validation (ICML 2001)[0m
[38;5;12m  - Hendrik Blockeel, Jan Struyf[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/papers/volume3/blockeel02a/blockeel02a.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBias Correction in Classification Tree Construction (ICML 2001)[0m
[38;5;12m  - Alin Dobra, Johannes Gehrke[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.cornell.edu/people/dobra/papers/icml2001-bias.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBreeding Decision Trees Using Evolutionary Techniques (ICML 2001)[0m
[38;5;12m  - Athanassios Papagelis, Dimitrios Kalles[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.gatree.com/data/BreedinDecisioTreeUsinEvo.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mObtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers (ICML 2001)[0m
[38;5;12m  - Bianca Zadrozny, Charles Elkan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://cseweb.ucsd.edu/~elkan/calibrated.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mTemporal Decision Trees or the lazy ECU vindicated (IJCAI 2001)[0m
[38;5;12m  - Luca Console, Claudia Picardi, Daniele Theseider Dupré[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/220815333_Temporal_Decision_Trees_or_the_lazy_ECU_vindicated)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mData Mining Criteria for Tree-based Regression and Classification (KDD 2001)[0m
[38;5;12m  - Andreas Buja, Yung-Seop Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://repository.upenn.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1&article=1406&context=statistics_papers)[39m

[38;5;12m- [39m[38;5;14m[1mA Decision Tree of Bigrams is an Accurate Predictor of Word Sense (NAACL 2001)[0m
[38;5;12m  - Ted Pedersen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/N01-1011)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mRule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron (PAKDD 2001)[0m
[38;5;12m  - DaeEun Kim, Jaeho Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=693490)[39m

[38;5;12m- [39m[38;5;14m[1mA Scalable Algorithm for Rule Post-pruning of Large Decision Trees (PAKDD 2001)[0m
[38;5;12m  - Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/3-540-45357-1_49)[39m

[38;5;12m- [39m[38;5;14m[1mOptimizing the Induction of Alternating Decision Trees (PAKDD 2001)[0m
[38;5;12m  - Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/33051701_Optimizing_the_Induction_of_Alternating_Decision_Trees)[39m

[38;5;12m- [39m[38;5;14m[1mInteractive Construction of Decision Trees (PAKDD 2001)[0m
[38;5;12m  - Jianchao Han, Nick Cercone[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pure.tue.nl/ws/files/3522084/672434611234867.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBloomy Decision Tree for Multi-objective Classification (PKDD 2001)[0m
[38;5;12m  - Einoshin Suzuki, Masafumi Gotoh, Yuta Choki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/3-540-44794-6_36)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Fourier Analysis Based Approach to Learning Decision Trees in a Distributed Environment (SDM 2001)[0m
[38;5;12m  - Byung-Hoon Park, Rajeev Ayyagari, Hillol Kargupta[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://archive.siam.org/meetings/sdm01/pdf/sdm01_19.pdf)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m2000[0m

[38;5;12m- [39m[38;5;14m[1mIntuitive Representation of Decision Trees Using General Rules and Exceptions (AAAI 2000)[0m
[38;5;12m  - Bing Liu, Minqing Hu, Wynne Hsu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/e284/96551e595f1850a53f93affa98919147712f.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mTagging Unknown Proper Names Using Decision Trees (ACL 2000)[0m
[38;5;12m  - Frédéric Béchet, Alexis Nasr, Franck Genet[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/P00-1011)[39m

[38;5;12m- [39m[38;5;14m[1mClustering Through Decision Tree Construction (CIKM 2000)[0m
[38;5;12m  - Bing Liu, Yiyuan Xia, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=354775)[39m

[38;5;12m- [39m[38;5;14m[1mHandling Continuous-Valued Attributes in Decision Tree with Neural Network Modelling (ECML 2000)[0m
[38;5;12m  - DaeEun Kim, Jaeho Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/content/pdf/10.1007/3-540-45164-1_22.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mInvestigation and Reduction of Discretization Variance in Decision Tree Induction (ECML 2000)[0m
[38;5;12m  - Pierre Geurts, Louis Wehenkel[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/3-540-45164-1_17)[39m

[38;5;12m- [39m[38;5;14m[1mNonparametric Regularization of Decision Trees (ECML 2000)[0m
[38;5;12m  - Tobias Scheffer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/3-540-45164-1_36)[39m

[38;5;12m- [39m[38;5;14m[1mExploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria (ICML 2000)[0m
[38;5;12m  - Chris Drummond, Robert C. Holte[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/160e/21c3acc925b60dc040cb1705e58bb166b045.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mMulti-agent Q-learning and Regression Trees for Automated Pricing Decisions (ICML 2000)[0m
[38;5;12m  - Manu Sridharan, Gerald Tesauro[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://manu.sridharan.net/files/icml00.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mGrowing Decision Trees on Support-less Association Rules (KDD 2000)[0m
[38;5;12m  - Ke Wang, Senqiang Zhou, Yu He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www2.cs.sfu.ca/~wangk/pub/kdd002.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mEfficient Algorithms for Constructing Decision Trees with Constraints (KDD 2000)[0m
[38;5;12m  - Minos N. Garofalakis, Dongjoon Hyun, Rajeev Rastogi, Kyuseok Shim[39m
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[38;5;12m- [39m[38;5;14m[1mInteractive Visualization in Mining Large Decision Trees (PAKDD 2000)[0m
[38;5;12m  - Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/content/pdf/10.1007/3-540-45571-X_40.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mVQTree: Vector Quantization for Decision Tree Induction (PAKDD 2000)[0m
[38;5;12m  - Shlomo Geva, Lawrence Buckingham[39m
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[38;5;12m- [39m[38;5;14m[1mSome Enhencements of Decision Tree Bagging (PKDD 2000)[0m
[38;5;12m  - Pierre Geurts[39m
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[38;5;12m- [39m[38;5;14m[1mCombining Multiple Models with Meta Decision Trees (PKDD 2000)[0m
[38;5;12m  - Ljupco Todorovski, Saso Dzeroski[39m
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[38;5;12m- [39m[38;5;14m[1mInduction of Multivariate Decision Trees by Using Dipolar Criteria (PKDD 2000)[0m
[38;5;12m  - Leon Bobrowski, Marek Kretowski[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/3-540-45372-5_33)[39m

[38;5;12m- [39m[38;5;14m[1mDecision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms (PKDD 2000)[0m
[38;5;12m  - Nikos Drossos, Athanassios Papagelis, Dimitrios Kalles[39m
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[38;2;255;187;0m[4m1999[0m
[38;5;12m- [39m[38;5;14m[1mModeling Decision Tree Performance with the Power Law (AISTATS 1999)[0m
[38;5;12m  - Lewis J. Frey, Douglas H. Fisher[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ModelingTree.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCausal Mechanisms and Classification Trees for Predicting Chemical Carcinogens (AISTATS 1999)[0m
[38;5;12m  - Louis Anthony Cox Jr.[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/0d7b/1d55c5abfd024aacf645c66d0c90c283814e.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mPOS Tags and Decision Trees for Language Modeling (EMNLP 1999)[0m
[38;5;12m  - Peter A. Heeman[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/W99-0617)[39m

[38;5;12m- [39m[38;5;14m[1mLazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)[0m
[38;5;12m  - Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting[39m
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[38;5;12m- [39m[38;5;14m[1mThe Alternating Decision Tree Learning Algorithm (ICML 1999)[0m
[38;5;12m  - Yoav Freund, Llew Mason[39m
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[38;5;12m- [39m[38;5;14m[1mBoosting with Multi-Way Branching in Decision Trees (NIPS 1999)[0m
[38;5;12m  - Yishay Mansour, David A. McAllester[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/1659-boosting-with-multi-way-branching-in-decision-trees.pdf)[39m

[38;2;255;187;0m[4m1998[0m
[38;5;12m- [39m[38;5;14m[1mLearning Sorting and Decision Trees with POMDPs (ICML 1998)[0m
[38;5;12m  - Blai Bonet, Hector Geffner[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://bonetblai.github.io/reports/icml98-learning.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUsing a Permutation Test for Attribute Selection in Decision Trees (ICML 1998)[0m
[38;5;12m  - Eibe Frank, Ian H. Witten[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/9aa9/21b0203e06e98b49bf726a33e124f4310ea3.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Fast and Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization (ICML 1998)[0m
[38;5;12m  - Michael J. Kearns, Yishay Mansour[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cis.upenn.edu/~mkearns/papers/pruning.pdf)[39m

[38;2;255;187;0m[4m1997[0m
[38;5;12m- [39m[38;5;14m[1mPessimistic Decision Tree Pruning Based Continuous-Time (ICML 1997)[0m
[38;5;12m  - Yishay Mansour[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/b6fc/e37612db10a9756b904b5e79e1144ca12574.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mPAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (ICML 1997)[0m
[38;5;12m  - Scott E. Decatur[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.semanticscholar.org/paper/PAC-Learning-with-Constant-Partition-Classification-Decatur/dd205073aeb512ecd1e823b35f556058fdeea5e0)[39m

[38;5;12m- [39m[38;5;14m[1mOption Decision Trees with Majority Votes (ICML 1997)[0m
[38;5;12m  - Ron Kohavi, Clayton Kunz[39m
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[38;5;12m- [39m[38;5;14m[1mIntegrating Feature Construction with Multiple Classifiers in Decision Tree Induction (ICML 1997)[0m
[38;5;12m  - Ricardo Vilalta, Larry A. Rendell[39m
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[38;5;12m- [39m[38;5;14m[1mFunctional Models for Regression Tree Leaves (ICML 1997)[0m
[38;5;12m  - Luís Torgo[39m
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[38;5;12m- [39m[38;5;14m[1mThe Effects of Training Set Size on Decision Tree Complexity (ICML 1997)[0m
[38;5;12m  - Tim Oates, David D. Jensen[39m
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[38;5;12m- [39m[38;5;14m[1mUnsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis (NIPS 1997)[0m
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[38;5;12m- [39m[38;5;14m[1mData-Dependent Structural Risk Minimization for Perceptron Decision Trees (NIPS 1997)[0m
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[38;5;12m- [39m[38;5;14m[1mGeneralization in Decision Trees and DNF: Does Size Matter (NIPS 1997)[0m
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[38;2;255;187;0m[4m1996[0m
[38;5;12m- [39m[38;5;14m[1mSecond Tier for Decision Trees (ICML 1996)[0m
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[38;5;12m- [39m[38;5;14m[1mNon-Linear Decision Trees - NDT (ICML 1996)[0m
[38;5;12m  - Andreas Ittner, Michael Schlosser[39m
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[38;5;12m- [39m[38;5;14m[1mLearning Relational Concepts with Decision Trees (ICML 1996)[0m
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[38;2;255;187;0m[4m1995[0m
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[38;5;12m- [39m[38;5;14m[1mAn Exact Probability Metric for Decision Tree Splitting (AISTATS 1995)[0m
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[38;5;12m- [39m[38;5;14m[1mOn Pruning and Averaging Decision Trees (ICML 1995)[0m
[38;5;12m  - Jonathan J. Oliver, David J. Hand[39m
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[38;5;12m- [39m[38;5;14m[1mOn Handling Tree-Structured Attributed in Decision Tree Learning (ICML 1995)[0m
[38;5;12m  - Hussein Almuallim, Yasuhiro Akiba, Shigeo Kaneda[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.sciencedirect.com/science/article/pii/B9781558603776500116)[39m

[38;5;12m- [39m[38;5;14m[1mRetrofitting Decision Tree Classifiers Using Kernel Density Estimation (ICML 1995)[0m
[38;5;12m  - Padhraic Smyth, Alexander G. Gray, Usama M. Fayyad[39m
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[38;5;12m- [39m[38;5;14m[1mIncreasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves (ICML 1995)[0m
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[38;5;12m- [39m[38;5;14m[1mEfficient Algorithms for Finding Multi-way Splits for Decision Trees (ICML 1995)[0m
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[38;5;12m- [39m[38;5;14m[1mTheory and Applications of Agnostic PAC-Learning with Small Decision Trees (ICML 1995)[0m
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[38;5;12m- [39m[38;5;14m[1mBoosting Decision Trees (NIPS 1995)[0m
[38;5;12m  - Harris Drucker, Corinna Cortes[39m
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[38;5;12m- [39m[38;5;14m[1mUsing Pairs of Data-Points to Define Splits for Decision Trees (NIPS 1995)[0m
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[38;5;12m- [39m[38;5;14m[1mA New Pruning Method for Solving Decision Trees and Game Trees (UAI 1995)[0m
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[38;2;255;187;0m[4m1994[0m
[38;5;12m- [39m[38;5;14m[1mA Statistical Approach to Decision Tree Modeling (ICML 1994)[0m
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[38;5;12m- [39m[38;5;14m[1mIn Defense of C4.5: Notes Learning One-Level Decision Trees (ICML 1994)[0m
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[38;5;12m- [39m[38;5;14m[1mAn Improved Algorithm for Incremental Induction of Decision Trees (ICML 1994)[0m
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[38;2;255;187;0m[4m1993[0m
[38;5;12m- [39m[38;5;14m[1mUsing Decision Trees to Improve Case-Based Learning (ICML 1993)[0m
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[38;5;12m- [39m[38;5;14m[1mContext Dependent Modeling of Phones in Continuous Speech Using Decision Trees (NAACL 1991)[0m
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[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1304.2732)[39m
[38;5;12m  [39m
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