awesome-awesomeness/terminal/decisiontreepapers

                                                                                        Awesome Decision, Classification, and Regression Tree Research Papers
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A curated list of classification and regression tree research papers with implementations from the following conferences:

- Machine learning
   ⟡ NeurIPS (https://nips.cc/) 
   ⟡ ICML (https://icml.cc/) 
   ⟡ ICLR (https://iclr.cc/)
- Computer vision
   ⟡ CVPR (http://cvpr2019.thecvf.com/)
   ⟡ ICCV (http://iccv2019.thecvf.com/)
   ⟡ ECCV (https://eccv2018.org/)
- Natural language processing
   ⟡ ACL (http://www.acl2019.org/EN/index.xhtml)
   ⟡ NAACL (https://naacl2019.org/)
   ⟡ EMNLP (https://www.emnlp-ijcnlp2019.org/) 
- Data
   ⟡ KDD (https://www.kdd.org/)
   ⟡ CIKM (http://www.cikmconference.org/)   
   ⟡ ICDM (http://icdm2019.bigke.org/)
   ⟡ SDM (https://www.siam.org/Conferences/CM/Conference/sdm19)   
   ⟡ PAKDD (http://pakdd2019.medmeeting.org)
   ⟡ PKDD/ECML (http://ecmlpkdd2019.org)
   ⟡ SIGIR (https://sigir.org/)
   ⟡ WWW (https://www2019.thewebconf.org/)
   ⟡ WSDM (www.wsdm-conference.org) 
- Artificial intelligence
   ⟡ AAAI (https://www.aaai.org/)
   ⟡ AISTATS (https://www.aistats.org/)
   ⟡ ICANN (https://e-nns.org/icann2019/)   
   ⟡ IJCAI (https://www.ijcai.org/)
   ⟡ UAI (http://www.auai.org/)

Similar collections about graph classification (https://github.com/benedekrozemberczki/awesome-graph-classification), gradient boosting (https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers), fraud detection 
(https://github.com/benedekrozemberczki/awesome-fraud-detection-papers), Monte Carlo tree search (https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers), and community detection 
(https://github.com/benedekrozemberczki/awesome-community-detection) papers with implementations.

2022

- Using MaxSAT for Efficient Explanations of Tree Ensembles (AAAI 2022)
  - Alexey Ignatiev, Yacine Izza, Peter J. Stuckey, João Marques-Silva
  - Paper  (https://alexeyignatiev.github.io/assets/pdf/iisms-aaai22-preprint.pdf)

- FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles (AAAI 2022)
  - Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke
  - Paper  (https://a-lucic.github.io/talks/ICML_SMRL_focus.pdf)

- Explainable and Local Correction of Classification Models Using Decision Trees (AAAI 2022)
  - Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Keisuke Goto, Yuta Fujishige, Satoshi Hara
  - Paper  (https://ojs.aaai.org/index.php/AAAI/article/view/20816)

- Robust Optimal Classification Trees against Adversarial Examples (AAAI 2022)
  - Daniël Vos, Sicco Verwer
  - Paper  (https://arxiv.org/abs/2109.03857)

- Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values (AAAI 2022)
  - Haewon Jeong, Hao Wang, Flávio P. Calmon
  - Paper  (https://arxiv.org/abs/2109.10431)

- Fast Sparse Decision Tree Optimization via Reference Ensembles (AAAI 2022)
  - Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer
  - Paper  (https://arxiv.org/abs/2112.00798)
  - Code  (https://pypi.org/project/gosdt/)

- TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)
  - Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu
  - Paper  (https://arxiv.org/abs/2112.02365)
  - Code  (https://github.com/yihengsun/TransBoost)

- Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees (AISTATS 2022)
  - Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike
  - Paper  (https://proceedings.mlr.press/v151/kanamori22a.html)

- Accurate Shapley Values for explaining tree-based models (AISTATS 2022)
  - Salim I. Amoukou, Tangi Salaün, Nicolas J.-B. Brunel
  - Paper  (https://arxiv.org/abs/2106.03820)

- A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds (AISTATS 2022)
  - Yan Shuo Tan, Abhineet Agarwal, Bin Yu
  - Paper  (https://arxiv.org/abs/2110.09626)
  - Code  (https://github.com/aagarwal1996/additive_trees)

- Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees (WWW 2022)
  - Philip A. Etter, Kai Zhong, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon
  - Paper  (https://arxiv.org/abs/2106.02697)

- MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration (WWW 2022)
  - Siguang Huang, Yunli Wang, Lili Mou, Huayue Zhang, Han Zhu, Chuan Yu, Bo Zheng
  - Paper  (https://arxiv.org/abs/2202.04348)

- Rethinking Conversational Recommendations: Is Decision Tree All You Need (CIKM 2022)
  - A S. M. Ahsan-Ul-Haque, Hongning Wang
  - Paper  (https://arxiv.org/abs/2208.14614)

- A Neural Tangent Kernel Perspective of Infinite Tree Ensembles (ICLR 2022)
  - Ryuichi Kanoh, Mahito Sugiyama
  - Paper  (https://openreview.net/forum?id=vUH85MOXO7h)
 
- POETREE: Interpretable Policy Learning with Adaptive Decision Trees (ICLR 2022)
  - Alizée Pace, Alex Chan, Mihaela van der Schaar
  - Paper  (https://arxiv.org/abs/2203.08057)

- Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models (ICML 2022)
  - Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu
  - Paper  (https://arxiv.org/abs/2202.00858)

- Popular decision tree algorithms are provably noise tolerant (ICML 2022)
  - Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan
  - Paper  (https://arxiv.org/abs/2206.08899)

- Robust Counterfactual Explanations for Tree-Based Ensembles (ICML 2022)
  - Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni
  - Paper  (https://proceedings.mlr.press/v162/dutta22a.html)

- Fast Provably Robust Decision Trees and Boosting (ICML 2022)
  - Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou
  - Paper  (https://proceedings.mlr.press/v162/guo22h.html)

- BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression (ICML 2022)
  - Zhao Tang Luo, Huiyan Sang, Bani K. Mallick
  - Paper  (https://proceedings.mlr.press/v162/luo22a.html)

- Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features (ICML 2022)
  - Rahul Mazumder, Xiang Meng, Haoyue Wang
  - Paper  (https://arxiv.org/abs/2206.11844)

- A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources (ICML 2022)
  - Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang
  - Paper  (https://arxiv.org/abs/2103.06261)

- On Preferred Abductive Explanations for Decision Trees and Random Forests (IJCAI 2022)
  - Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
  - Paper  (https://www.ijcai.org/proceedings/2022/0091.pdf)

- Extending Decision Tree to Handle Multiple Fairness Criteria (IJCAI 2022)
  - Alessandro Castelnovo
  - Paper  (https://www.ijcai.org/proceedings/2022/0822.pdf)

- Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles (KDD 2022)
  - Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder
  - Paper  (https://arxiv.org/abs/2205.09717)

- Integrity Authentication in Tree Models (KDD 2022)
  - Weijie Zhao, Yingjie Lao, Ping Li
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3534678.3539428)

- Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)
  - Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3534678.3539052)

- Improved feature importance computation for tree models based on the Banzhaf value (UAI 2022)
  - Adam Karczmarz, Tomasz Michalak, Anish Mukherjee, Piotr Sankowski, Piotr Wygocki
  - Paper  (https://proceedings.mlr.press/v180/karczmarz22a.html)

- Learning linear non-Gaussian polytree models (UAI 2022)
  - Daniele Tramontano, Anthea Monod, Mathias Drton
  - Paper  (https://arxiv.org/abs/2208.06701)

2021

- Online Probabilistic Label Trees (AISTATS 2021)
  - Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczyński
  - Paper  (https://arxiv.org/abs/2007.04451)
  - Code  (https://github.com/mwydmuch/napkinXC)

- Optimal Decision Trees for Nonlinear Metrics (AAAI 2021)
  - Emir Demirovic, Peter J. Stuckey
  - Paper  (https://arxiv.org/abs/2009.06921)

- SAT-based Decision Tree Learning for Large Data Sets (AAAI 2021)
  - André Schidler, Stefan Szeider
  - Paper  (https://ojs.aaai.org/index.php/AAAI/article/view/16509)

- Parameterized Complexity of Small Decision Tree Learning (AAAI 2021)
  - Sebastian Ordyniak, Stefan Szeider
  - Paper  (https://www.ac.tuwien.ac.at/files/tr/ac-tr-21-002.pdf)

- Counterfactual Explanations for Oblique Decision Trees: Exact - Efficient Algorithms (AAAI 2021)
  - Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
  - Paper  (https://arxiv.org/abs/2103.01096)

- Geometric Heuristics for Transfer Learning in Decision Trees (CIKM 2021)
  - Siddhesh Chaubal, Mateusz Rzepecki, Patrick K. Nicholson, Guangyuan Piao, Alessandra Sala
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3459637.3482259)

- Fairness-Aware Training of Decision Trees by Abstract Interpretation (CIKM 2021)
  - Francesco Ranzato, Caterina Urban, Marco Zanella
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3459637.3482342)

- Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification (CIKM 2021)
  - Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon
  - Paper  (https://arxiv.org/abs/2106.00730)

- Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)
  - Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork
  - Paper  (https://openreview.net/forum?id=Ut1vF_q_vC)

- NBDT: Neural-Backed Decision Tree (ICLR 2021)
  - Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez
  - Paper  (https://arxiv.org/abs/2004.00221)

- Versatile Verification of Tree Ensembles (ICML 2021)
  - Laurens Devos, Wannes Meert, Jesse Davis
  - Paper  (https://arxiv.org/abs/2010.13880)

- Connecting Interpretability and Robustness in Decision Trees through Separation (ICML 2021)
  - Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
  - Paper  (https://arxiv.org/abs/2102.07048)

- Optimal Counterfactual Explanations in Tree Ensembles (ICML 2021)
  - Axel Parmentier, Thibaut Vidal
  - Paper  (https://arxiv.org/abs/2106.06631)

- Efficient Training of Robust Decision Trees Against Adversarial Examples (ICML 2021)
  - Daniël Vos, Sicco Verwer
  - Paper  (https://arxiv.org/abs/2012.10438)

- Learning Binary Decision Trees by Argmin Differentiation (ICML 2021)
  - Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
  - Paper  (https://arxiv.org/pdf/2010.04627.pdf)
 
- BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment (KDD 2021)
  - Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua T. Vogelstein, Randal Burns
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3447548.3467368)

- ControlBurn: Feature Selection by Sparse Forests (KDD 2021)
  - Brian Liu, Miaolan Xie, Madeleine Udell
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3447548.3467387?sid=SCITRUS)

- Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)
  - Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
  - Paper  (https://dl.acm.org/doi/10.1145/3447548.3467278)

- Verifying Tree Ensembles by Reasoning about Potential Instances (SDM 2021)
  - Laurens Devos, Wannes Meert, Jesse Davis
  - Paper  (https://arxiv.org/abs/2001.11905)

2020

- DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (ACL 2020)
  - Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, Ambreen Nazir
  - Paper  (https://arxiv.org/abs/2004.13455)

- Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)
  - Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He
  - Paper  (https://arxiv.org/abs/1911.04209)

- Practical Federated Gradient Boosting Decision Trees (AAAI 2020)
  - Qinbin Li, Zeyi Wen, Bingsheng He
  - Paper  (https://arxiv.org/abs/1911.04206)

- Efficient Inference of Optimal Decision Trees (AAAI 2020)
  - Florent Avellaneda
  - Paper  (http://florent.avellaneda.free.fr/dl/AAAI20.pdf)
  - Code  (https://github.com/FlorentAvellaneda/InferDT)

- Learning Optimal Decision Trees Using Caching Branch-and-Bound Search (AAAI 2020)
  - Gael Aglin, Siegfried Nijssen, Pierre Schaus
  - Paper  (https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A223390/datastream/PDF_01/view)
  - Code  (https://pypi.org/project/dl8.5/)

- Abstract Interpretation of Decision Tree Ensemble Classifiers (AAAI 2020)
  - Francesco Ranzato, Marco Zanella
  - Paper  (https://www.math.unipd.it/~ranzato/papers/aaai20.pdf)
  - Code  (https://github.com/abstract-machine-learning/silva)

- Scalable Feature Selection for (Multitask) Gradient Boosted Trees (AISTATS 2020)
  - Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian
  - Paper  (http://proceedings.mlr.press/v108/han20a.html)

- Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning (AISTATS 2020)
  - Andrew Silva, Matthew C. Gombolay, Taylor W. Killian, Ivan Dario Jimenez Jimenez, Sung-Hyun Son
  - Paper  (https://arxiv.org/abs/1903.09338)

- Exploiting Categorical Structure Using Tree-Based Methods (AISTATS 2020)
  - Brian Lucena
  - Paper  (https://arxiv.org/abs/2004.07383)

- LdSM: Logarithm-depth Streaming Multi-label Decision Trees (AISTATS 2020)
  - Maryam Majzoubi, Anna Choromanska
  - Paper  (https://arxiv.org/abs/1905.10428)
  
- Oblique Decision Trees from Derivatives of ReLU Networks (ICLR 2020)
  - Guang-He Lee, Tommi S. Jaakkola
  - Paper  (https://openreview.net/pdf?id=Bke8UR4FPB)
  - Code  (https://github.com/guanghelee/iclr20-lcn)
  
- Provable Guarantees for Decision Tree Induction: the Agnostic Setting (ICML 2020)
  - Guy Blanc, Jane Lange, Li-Yang Tan
  - Paper  (https://arxiv.org/abs/2006.00743v1)

- Decision Trees for Decision-Making under the Predict-then-Optimize Framework (ICML 2020)
  - Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis
  - Paper  (https://arxiv.org/abs/2003.00360)

- The Tree Ensemble Layer: Differentiability meets Conditional Computation (ICML 2020)
  - Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder
  - Paper  (https://arxiv.org/abs/2002.07772)
  - Code  (https://github.com/google-research/google-research/tree/master/tf_trees)

- Generalized and Scalable Optimal Sparse Decision Trees (ICML 2020)
  - Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer
  - Paper  (https://arxiv.org/abs/2006.08690)
  - Code  (https://github.com/xiyanghu/OSDT)

- Born-Again Tree Ensembles (ICML 2020)
  - Thibaut Vidal, Maximilian Schiffer
  - Paper  (https://arxiv.org/abs/2003.11132)
  - Code  (https://github.com/vidalt/BA-Trees)

- On Lp-norm Robustness of Ensemble Decision Stumps and Trees (ICML 2020)
  - Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh
  - Paper  (https://arxiv.org/abs/2008.08755)

- Smaller, More Accurate Regression Forests Using Tree Alternating Optimization (ICML 2020)
  - Arman Zharmagambetov, Miguel Á. Carreira-Perpinan
  - Paper  (http://proceedings.mlr.press/v119/zharmagambetov20a.html)
  
- Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)
  - Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet
  - Paper  (https://www.ijcai.org/Proceedings/2020/163)

- Speeding up Very Fast Decision Tree with Low Computational Cost (IJCAI 2020)
  - Jian Sun, Hongyu Jia, Bo Hu, Xiao Huang, Hao Zhang, Hai Wan, Xibin Zhao
  - Paper  (https://www.ijcai.org/Proceedings/2020/0177.pdf)

- PyDL8.5: a Library for Learning Optimal Decision Trees (IJCAI 2020)
  - Gaël Aglin, Siegfried Nijssen, Pierre Schaus
  - Paper  (https://www.ijcai.org/Proceedings/2020/0750.pdf)
  - Code  (https://github.com/aia-uclouvain/pydl8.5)

- Geodesic Forests (KDD 2020)
  - Meghana Madhyastha, Gongkai Li, Veronika Strnadova-Neeley, James Browne, Joshua T. Vogelstein, Randal Burns
  - Paper  (https://dl.acm.org/doi/pdf/10.1145/3394486.3403094)

- A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (NeurIPS 2020)
  - Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam
  - Paper  (https://arxiv.org/abs/2011.03375)

- Estimating Decision Tree Learnability with Polylogarithmic Sample Complexity (NeurIPS 2020)
  - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
  - Paper  (https://arxiv.org/abs/2011.01584)
 
- Universal Guarantees for Decision Tree Induction via a Higher-Order Splitting Criterion (NeurIPS 2020)
  - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
  - Paper  (https://arxiv.org/abs/2010.08633)

- Smooth And Consistent Probabilistic Regression Trees (NeurIPS 2020)
  - Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Éric Gaussier, Georges Oppenheim
  - Paper  (https://papers.nips.cc/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf)

- An Efficient Adversarial Attack for Tree Ensembles (NeurIPS 2020)
  - Chong Zhang, Huan Zhang, Cho-Jui Hsieh
  - Paper  (https://arxiv.org/abs/2010.11598)
  - Code  (https://github.com/chong-z/tree-ensemble-attack)

- Decision Trees as Partitioning Machines to Characterize their Generalization Properties (NeurIPS 2020)
  - Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
  - Paper  (https://papers.nips.cc/paper/2020/file/d2a10b0bd670e442b1d3caa3fbf9e695-Paper.pdf)
  
- Evidence Weighted Tree Ensembles for Text Classification (SIGIR 2020)
  - Md. Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu, Wei Kang
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3397271.3401229)
  
2019

- Multi Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System (AAAI 2019)
  - Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang
  - Paper  (https://arxiv.org/pdf/1805.09484.pdf)
  
- Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)
  - Farhad Shakerin, Gopal Gupta
  - Paper  (https://arxiv.org/abs/1808.00629)
  
- Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (AAAI 2019)
  - Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos
  - Paper  (https://arxiv.org/abs/1903.10598)

- Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals (AAAI 2019)
  - Kacper Sokol, Peter A. Flach
  - Paper  (https://aaai.org/ojs/index.php/AAAI/article/view/5154)
  
- Weighted Oblique Decision Trees (AAAI 2019)
  - Bin-Bin Yang, Song-Qing Shen, Wei Gao
  - Paper  (https://aaai.org/ojs/index.php/AAAI/article/view/4505)

- Learning Optimal Classification Trees Using a Binary Linear Program Formulation (AAAI 2019)
  - Sicco Verwer, Yingqian Zhang
  - Paper  (https://yingqianzhang.net/wp-content/uploads/2018/12/VerwerZhangAAAI-final.pdf)

- Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees (AAAI 2019)
  - Pooya Tavallali, Peyman Tavallali, Mukesh Singhal
  - Paper  (https://aaai.org/ojs/index.php/AAAI/article/view/4447/4325)

- XBART: Accelerated Bayesian Additive Regression Trees (AISTATS 2019)
  - Jingyu He, Saar Yalov, P. Richard Hahn
  - Paper  (https://arxiv.org/abs/1810.02215)

- Interaction Detection with Bayesian Decision Tree Ensembles (AISTATS 2019)
  - Junliang Du, Antonio R. Linero
  - Paper  (https://arxiv.org/abs/1809.08524)
  
- Adversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)
  - Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei
  - Paper  (https://www.dais.unive.it/~calzavara/papers/cikm19.pdf)

- Interpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)
  - Ya-Lin Zhang, Longfei Li
  - Paper  (https://dl.acm.org/citation.cfm?id=3357384.3358072)

- Interpreting CNNs via Decision Trees (CVPR 2019)
  - Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu
  - Paper  (https://arxiv.org/abs/1802.00121)
  
- EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees (ICDM 2019)
  - Jaemin Yoo, Lee Sael
  - Paper  (https://github.com/leesael/EDiT/blob/master/docs/YooS19.pdf)
  - Code  (https://github.com/leesael/EDiT)

- Fair Adversarial Gradient Tree Boosting (ICDM 2019)
  - Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki
  - Paper  (https://arxiv.org/abs/1911.05369)

- Functional Transparency for Structured Data: a Game-Theoretic Approach (ICML 2019)
  - Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
  - Paper  (http://proceedings.mlr.press/v97/lee19b/lee19b.pdf)
  
- Incorporating Grouping Information into Bayesian Decision Tree Ensembles (ICML 2019)
  - Junliang Du, Antonio R. Linero
  - Paper  (http://proceedings.mlr.press/v97/du19d.html)

- Adaptive Neural Trees (ICML 2019)
  - Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori
  - Paper  (https://arxiv.org/abs/1807.06699)
  - Code  (https://github.com/rtanno21609/AdaptiveNeuralTrees)

- Robust Decision Trees Against Adversarial Examples (ICML 2019)
  - Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh
  - Paper  (https://arxiv.org/abs/1902.10660)
  - Code  (https://github.com/chenhongge/RobustTrees)
  
- Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples (IJCAI 2019)
  - Ariyam Das, Jin Wang, Sahil M. Gandhi, Jae Lee, Wei Wang, Carlo Zaniolo
  - Paper  (https://www.ijcai.org/proceedings/2019/0306.pdf)
  
- FAHT: An Adaptive Fairness-aware Decision Tree Classifier (IJCAI 2019)
  - Wenbin Zhang, Eirini Ntoutsi
  - Paper  (https://arxiv.org/abs/1907.07237)
  - Code  (https://github.com/vanbanTruong/FAHT)

- Inter-node Hellinger Distance based Decision Tree (IJCAI 2019)
  - Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib
  - Paper  (https://www.ijcai.org/proceedings/2019/0272.pdf)
  - Matlab Code  (https://github.com/ZDanielsResearch/HellingerTreesMatlab)
  - R Code  (https://github.com/kaustubhrpatil/HDDT)

- Gradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)
  - Yu Shi, Jian Li, Zhize Li
  - Paper  (https://arxiv.org/abs/1802.05640)
  - Code  (https://github.com/GBDT-PL/GBDT-PL)

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

- Partitioning Structure Learning for Segmented Linear Regression Trees (NeurIPS 2019)
  - Xiangyu Zheng, Song Xi Chen
  - Paper  (https://papers.nips.cc/paper/8494-partitioning-structure-learning-for-segmented-linear-regression-trees)
  
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)
  - Maksym Andriushchenko, Matthias Hein
  - Paper  (https://arxiv.org/abs/1906.03526)
  - Code  (https://github.com/max-andr/provably-robust-boosting)

- Optimal Decision Tree with Noisy Outcomes (NeurIPS 2019)
  - Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R. Ravi
  - Paper  (https://papers.nips.cc/paper/8592-optimal-decision-tree-with-noisy-outcomes.pdf)
  - Code  (https://github.com/sjia1/ODT-with-noisy-outcomes)

- Regularized Gradient Boosting (NeurIPS 2019)
  - Corinna Cortes, Mehryar Mohri, Dmitry Storcheus
  - Paper  (https://papers.nips.cc/paper/8784-regularized-gradient-boosting.pdf)

- Optimal Sparse Decision Trees (NeurIPS 2019)
  - Xiyang Hu, Cynthia Rudin, Margo Seltzer
  - Paper  (https://papers.nips.cc/paper/8947-optimal-sparse-decision-trees.pdf)
  - Code  (https://github.com/xiyanghu/OSDT)
  
- MonoForest framework for tree ensemble analysis (NeurIPS 2019)
  - Igor Kuralenok, Vasilii Ershov, Igor Labutin
  - Paper  (https://papers.nips.cc/paper/9530-monoforest-framework-for-tree-ensemble-analysis)
  - Code  (https://github.com/xiyanghu/OSDT)

- Calibrating Probability Estimation Trees using Venn-Abers Predictors (SDM 2019)
  - Ulf Johansson, Tuwe Löfström, Henrik Boström
  - Paper  (https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.4)
  
- Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization (SDM 2019)
  - Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander
  - Paper  (https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.32)

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

2018
- Adapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees (AAAI 2018)
  - Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M. Winands, Ann Nowé
  - Paper  (https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16183/16394)

- MERCS: Multi-Directional Ensembles of Regression and Classification Trees (AAAI 2018)
  - Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel
  - Paper  (https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16875/16735)
  - Code  (https://github.com/eliavw/mercs-v5)

- Differential Performance Debugging With Discriminant Regression Trees (AAAI 2018)
  - Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Ashutosh Trivedi
  - Paper  (https://arxiv.org/abs/1711.04076)
  - Code  (https://github.com/cuplv/DPDEBUGGER)

- Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction (AAAI 2018)
  - Jessa Bekker, Jesse Davis
  - Paper  (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16776)

- MDP-Based Cost Sensitive Classification Using Decision Trees (AAAI 2018)
  - Shlomi Maliah, Guy Shani
  - Paper  (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17128)

- Generative Adversarial Image Synthesis With Decision Tree Latent Controller (CVPR 2018)
  - Takuhiro Kaneko, Kaoru Hiramatsu, Kunio Kashino
  - Paper  (https://arxiv.org/abs/1805.10603)
  - Code  (https://github.com/LynnHo/DTLC-GAN-Tensorflow)

- Enhancing Very Fast Decision Trees with Local Split-Time Predictions (ICDM 2018)
  - Viktor Losing, Heiko Wersing, Barbara Hammer
  - Paper  (https://www.techfak.uni-bielefeld.de/~hwersing/LosingHammerWersing_ICDM2018.pdf)
  - Code  (https://github.com/ICDM2018Submission/VFDT-split-time-prediction)
  
- Realization of Random Forest for Real-Time Evaluation through Tree Framing (ICDM 2018)
  - Sebastian Buschjäger, Kuan-Hsun Chen, Jian-Jia Chen, Katharina Morik
  - Paper  (https://sfb876.tu-dortmund.de/PublicPublicationFiles/buschjaeger_2018a.pdf)

- Finding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)
  - Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke
  - Paper  (https://arxiv.org/abs/1802.06640)
  - Code  (https://github.com/bsharchilev/influence_boosting)

- Learning Optimal Decision Trees with SAT (IJCAI 2018)
  - Nina Narodytska, Alexey Ignatiev, Filipe Pereira, João Marques-Silva
  - Paper  (https://www.ijcai.org/proceedings/2018/0189.pdf)

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

- Alternating Optimization of Decision Trees with Application to Learning Sparse Oblique Trees (NIPS 2018)
  - Miguel Á. Carreira-Perpiñán, Pooya Tavallali
  - Paper  (https://papers.nips.cc/paper/7397-alternating-optimization-of-decision-trees-with-application-to-learning-sparse-oblique-trees)

- Multi-Layered Gradient Boosting Decision Trees (NIPS 2018)
  - Ji Feng, Yang Yu, Zhi-Hua Zhou
  - Paper  (https://papers.nips.cc/paper/7614-multi-layered-gradient-boosting-decision-trees.pdf)
  - Code  (https://github.com/kingfengji/mGBDT)
  
- Transparent Tree Ensembles (SIGIR 2018)
  - Alexander Moore, Vanessa Murdock, Yaxiong Cai, Kristine Jones
  - Paper  
(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_a29816c683aa83a0ce0fbb777c68daba)

- Privacy-aware Ranking with Tree Ensembles on the Cloud (SIGIR 2018)
  - Shiyu Ji, Jinjin Shao, Daniel Agun, Tao Yang
  - Paper  (https://sites.cs.ucsb.edu/projects/ds/sigir18.pdf)

2017
- Strategic Sequences of Arguments for Persuasion Using Decision Trees (AAAI 2017)
  - Emmanuel Hadoux, Anthony Hunter
  - Paper  (http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/aaai17.pdf)

- BoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)
  - Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales
  - Paper  (https://melmeric.files.wordpress.com/2010/05/boostvht-boosting-distributed-streaming-decision-trees.pdf)

- Latency Reduction via Decision Tree Based Query Construction (CIKM 2017)
  - Aman Grover, Dhruv Arya, Ganesh Venkataraman
  - Paper  (https://dl.acm.org/citation.cfm?id=3132865)

- Enumerating Distinct Decision Trees (ICML 2017)
  - Salvatore Ruggieri
  - Paper  (http://proceedings.mlr.press/v70/ruggieri17a/ruggieri17a.pdf)

- Gradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)
  - Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh
  - Paper  (http://proceedings.mlr.press/v70/si17a.html)
  - Code  (https://github.com/springdaisy/GBDT)

- Consistent Feature Attribution for Tree Ensembles (ICML 2017)
  - Scott M. Lundberg, Su-In Lee
  - Paper  (https://arxiv.org/abs/1706.06060)
  - Code  (https://github.com/slundberg/shap)

- Extremely Fast Decision Tree Mining for Evolving Data Streams (KDD 2017)
  - Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer
  - Paper  (https://core.ac.uk/download/pdf/151040580.pdf)
  
- CatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)
  - Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
  - Paper  (https://arxiv.org/abs/1810.11363)
  - Code  (https://catboost.ai/)

- LightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)
  - Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu
  - Paper  (https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)
  - Code  (https://lightgbm.readthedocs.io/en/latest/)

- Variable Importance Using Decision Trees (NIPS 2017)
  - Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
  - Paper  (https://papers.nips.cc/paper/6646-variable-importance-using-decision-trees)

- A Unified Approach to Interpreting Model Predictions (NIPS 2017)
  - Scott M. Lundberg, Su-In Lee
  - Paper  (https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions)
  - Code  (https://github.com/slundberg/shap)

- Pruning Decision Trees via Max-Heap Projection (SDM 2017)
  - Zhi Nie, Binbin Lin, Shuai Huang, Naren Ramakrishnan, Wei Fan, Jieping Ye
  - Paper  (https://www.researchgate.net/publication/317485748_Pruning_Decision_Trees_via_Max-Heap_Projection)

- A Practical Method for Solving Contextual Bandit Problems Using Decision Trees (UAI 2017)
  - Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik
  - Paper  (https://arxiv.org/abs/1706.04687)

- Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility (UAI 2017)
  - Hugo Gilbert, Olivier Spanjaard
  - Paper  (http://auai.org/uai2017/proceedings/papers/64.pdf)
  
- GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)
  - Qian Zhao, Yue Shi, Liangjie Hong
  - Paper  (http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1311.pdf)

2016
- Sparse Perceptron Decision Tree for Millions of Dimensions (AAAI 2016)
  - Weiwei Liu, Ivor W. Tsang
  - Paper  (https://aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12111)

- Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees (CVPR 2016)
  - Jianhui Chen, Hoang Minh Le, Peter Carr, Yisong Yue, James J. Little
  - Paper  (http://hoangle.info/papers/cvpr2016_online_smooth_long.pdf)

- Online Learning with Bayesian Classification Trees (CVPR 2016)
  - Samuel Rota Bulò, Peter Kontschieder
  - Paper  (http://www.dsi.unive.it/~srotabul/files/publications/CVPR2016.pdf)

- Accurate Robust and Efficient Error Estimation for Decision Trees (ICML 2016)
  - Lixin Fan
  - Paper  (http://proceedings.mlr.press/v48/fan16.pdf)

- Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)
  - Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
  - Paper  (http://proceedings.mlr.press/v48/ustinovskiy16.html)

- Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)
  - Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov
  - Paper  (https://www.kdd.org/kdd2016/papers/files/adf0653-poyarkovA.pdf)
  
- XGBoost: A Scalable Tree Boosting System (KDD 2016)
  - Tianqi Chen, Carlos Guestrin
  - Paper  (https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf)
  - Code  (https://xgboost.readthedocs.io/en/latest/)

- Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale (NIPS 2016)
  - Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar
  - Paper  (https://papers.nips.cc/paper/6366-yggdrasil-an-optimized-system-for-training-deep-decision-trees-at-scale)

- A Communication-Efficient Parallel Algorithm for Decision Tree (NIPS 2016)
  - Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu
  - Paper  (https://arxiv.org/abs/1611.01276)
  - Code  (https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst)

- Exploiting CPU SIMD Extensions to Speed-up Document Scoring with Tree Ensembles (SIGIR 2016)
  - Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
  - Paper  (http://pages.di.unipi.it/rossano/wp-content/uploads/sites/7/2016/07/SIGIR16a.pdf)
  - Code  (https://github.com/hpclab/vectorized-quickscorer)

- Post-Learning Optimization of Tree Ensembles for Efficient Ranking (SIGIR 2016)
  - Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, Salvatore Trani
  - Paper  (https://www.researchgate.net/publication/305081572_Post-Learning_Optimization_of_Tree_Ensembles_for_Efficient_Ranking)
  - Code  (https://github.com/hpclab/quickrank)

2015
- Particle Gibbs for Bayesian Additive Regression Trees (AISTATS 2015)
  - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
  - Paper  (https://arxiv.org/abs/1502.04622)

- DART: Dropouts Meet Multiple Additive Regression Trees (AISTATS 2015)
  - Korlakai Vinayak Rashmi, Ran Gilad-Bachrach
  - Paper  (https://arxiv.org/abs/1505.01866)
  - Code  (https://xgboost.readthedocs.io/en/latest/)

- Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-level Appearance Models (CVPR 2015)
  - Jingjing Xiao, Rustam Stolkin, Ales Leonardis
  - Paper  (https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_058.pdf)

- Face Alignment Using Cascade Gaussian Process Regression Trees (CVPR 2015)
  - Donghoon Lee, Hyunsin Park, Chang Dong Yoo
  - Paper  (https://slsp.kaist.ac.kr/paperdata/Face_Alignment_Using.pdf)
  - Code  (https://github.com/donghoonlee04/cGPRT)

- Tracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)
  - Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han
  - Paper  (https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Son_Tracking-by-Segmentation_With_Online_ICCV_2015_paper.pdf)

- Entropy Evaluation Based on Confidence Intervals of Frequency Estimates : Application to the Learning of Decision Trees (ICML 2015)
  - Mathieu Serrurier, Henri Prade
  - Paper  (http://proceedings.mlr.press/v37/serrurier15.pdf)
  
- Large-scale Distributed Dependent Nonparametric Trees (ICML 2015)
  - Zhiting Hu, Qirong Ho, Avinava Dubey, Eric P. Xing
  - Paper  (https://www.cs.cmu.edu/~zhitingh/data/icml15hu.pdf)

- Optimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)
  - Zhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen
  - Paper  (https://www.cse.wustl.edu/~ychen/public/OAE.pdf)

- A Decision Tree Framework for Spatiotemporal Sequence Prediction (KDD 2015)
  - Taehwan Kim, Yisong Yue, Sarah L. Taylor, Iain A. Matthews
  - Paper  (http://www.yisongyue.com/publications/kdd2015_ssw_dt.pdf)

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

2014

- A Mixtures-of-Trees Framework for Multi-Label Classification (CIKM 2014)
  - Charmgil Hong, Iyad Batal, Milos Hauskrecht
  - Paper  (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410801/)
  
- On Building Decision Trees from Large-scale Data in Applications of On-line Advertising (CIKM 2014)
  - Shivaram Kalyanakrishnan, Deepthi Singh, Ravi Kant
  - Paper  (https://www.cse.iitb.ac.in/~shivaram/papers/ksk_cikm_2014.pdf)

- Fast Supervised Hashing with Decision Trees for High-Dimensional Data (CVPR 2014)
  - Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter
  - Paper  (https://arxiv.org/abs/1404.1561)

- One Millisecond Face Alignment with an Ensemble of Regression Trees (CVPR 2014)
  - Vahid Kazemi, Josephine Sullivan
  - Paper  (https://www.researchgate.net/publication/264419855_One_Millisecond_Face_Alignment_with_an_Ensemble_of_Regression_Trees)
  
- The return of AdaBoost.MH: multi-class Hamming trees (ICLR 2014)
  - Balázs Kégl
  - Paper  (https://arxiv.org/pdf/1312.6086.pdf)

- Diagnosis Determination: Decision Trees Optimizing Simultaneously Worst and Expected Testing Cost (ICML 2014)
  - Ferdinando Cicalese, Eduardo Sany Laber, Aline Medeiros Saettler
  - Paper  (https://pdfs.semanticscholar.org/47ae/852f83b76f95b27ab00308d04f6020bdf71f.pdf)
  
- Learning Multiple-Question Decision Trees for Cold-Start Recommendation (WSDM 2013)
  - Mingxuan Sun, Fuxin Li, Joonseok Lee, Ke Zhou, Guy Lebanon, Hongyuan Zha
  - Paper  (http://www.joonseok.net/papers/coldstart.pdf)

2013
- Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria (ICCV 2013)
  - Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht
  - Paper  (https://ieeexplore.ieee.org/document/6751340)

- Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees (ICCV 2013)
  - Oisin Mac Aodha, Gabriel J. Brostow
  - Paper  (https://ieeexplore.ieee.org/document/6751133)

- Conformal Prediction Using Decision Trees (ICDM 2013)
  - Ulf Johansson, Henrik Boström, Tuve Löfström
  - Paper  (https://ieeexplore.ieee.org/abstract/document/6729517)

- Focal-Test-Based Spatial Decision Tree Learning: A Summary of Results (ICDM 2013)
  - Zhe Jiang, Shashi Shekhar, Xun Zhou, Joseph K. Knight, Jennifer Corcoran
  - Paper  (https://pdfs.semanticscholar.org/f28e/df8d9eed76e4ce97cb6bd4182d590547be5e.pdf)

- Top-down Particle Filtering for Bayesian Decision Trees (ICML 2013)
  - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
  - Paper  (https://arxiv.org/abs/1303.0561)

- Quickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)
  - Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona
  - Paper  (http://proceedings.mlr.press/v28/appel13.pdf)

- Knowledge Compilation for Model Counting: Affine Decision Trees (IJCAI 2013)
  - Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, Samuel Thomas
  - Paper  (https://www.researchgate.net/publication/262398921_Knowledge_Compilation_for_Model_Counting_Affine_Decision_Trees)
  
- Understanding Variable Importances in Forests of Randomized Trees (NIPS 2013)
  - Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
  - Paper  (https://papers.nips.cc/paper/4928-understanding-variable-importances-in-forests-of-randomized-trees)

- Regression-tree Tuning in a Streaming Setting (NIPS 2013)
  - Samory Kpotufe, Francesco Orabona
  - Paper  (https://papers.nips.cc/paper/4898-regression-tree-tuning-in-a-streaming-setting)

- Learning Max-Margin Tree Predictors (UAI 2013)
  - Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson
  - Paper  (https://ttic.uchicago.edu/~meshi/papers/mtreen.pdf)

2012
- Regression Tree Fields - An Efficient, Non-parametric Approach to Image Labeling Problems (CVPR 2012)
  - Jeremy Jancsary, Sebastian Nowozin, Toby Sharp, Carsten Rother
  - Paper  (http://www.nowozin.net/sebastian/papers/jancsary2012rtf.pdf)

- ConfDTree: Improving Decision Trees Using Confidence Intervals (ICDM 2012)
  - Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek
  - Paper  (https://ieeexplore.ieee.org/document/6413889)

- Improved Information Gain Estimates for Decision Tree Induction (ICML 2012)
  - Sebastian Nowozin
  - Paper  (https://arxiv.org/abs/1206.4620)

- Learning Partially Observable Models Using Temporally Abstract Decision Trees (NIPS 2012)
  - Erik Talvitie
  - Paper  (https://papers.nips.cc/paper/4662-learning-partially-observable-models-using-temporally-abstract-decision-trees)
  
- Subtree Replacement in Decision Tree Simplification (SDM 2012)
  - Salvatore Ruggieri
  - Paper  (http://pages.di.unipi.it/ruggieri/Papers/sdm2012.pdf)

2011
- Incorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)
  - Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich
  - Paper  (https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/viewFile/3711/4086)

- Syntactic Decision Tree LMs: Random Selection or Intelligent Design (EMNLP 2011)
  - Denis Filimonov, Mary P. Harper
  - Paper  (https://www.aclweb.org/anthology/D11-1064)
  
- Decision Tree Fields (ICCV 2011)
  - Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, Pushmeet Kohli
  - Paper  (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/nrbsyk_iccv11.pdf)

- Confidence in Predictions from Random Tree Ensembles (ICDM 2011)
  - Siddhartha Bhattacharyya
  - Paper  (https://link.springer.com/article/10.1007/s10115-012-0600-z)

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

- On the Complexity of Decision Making in Possibilistic Decision Trees (UAI 2011)
  - Hélène Fargier, Nahla Ben Amor, Wided Guezguez
  - Paper  (https://dslpitt.org/uai/papers/11/p203-fargier.pdf)

- Adaptive Bootstrapping of Recommender Systems Using Decision Trees (WSDM 2011)
  - Nadav Golbandi, Yehuda Koren, Ronny Lempel
  - Paper  (https://dl.acm.org/citation.cfm?id=1935910)

- Parallel Boosted Regression Trees for Web Search Ranking (WWW 2011)
  - Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin
  - Paper  (http://www.cs.cornell.edu/~kilian/papers/fr819-tyreeA.pdf)

2010
- Discrimination Aware Decision Tree Learning (ICDM 2010)
  - Faisal Kamiran, Toon Calders, Mykola Pechenizkiy
  - Paper  (https://www.win.tue.nl/~mpechen/publications/pubs/KamiranICDM2010.pdf)

- Decision Trees for Uplift Modeling (ICDM 2010)
  - Piotr Rzepakowski, Szymon Jaroszewicz
  - Paper  (https://core.ac.uk/download/pdf/81899141.pdf)

- Learning Markov Network Structure with Decision Trees (ICDM 2010)
  - Daniel Lowd, Jesse Davis
  - Paper  (https://ix.cs.uoregon.edu/~lowd/icdm10lowd.pdf)

- Multivariate Dyadic Regression Trees for Sparse Learning Problems (NIPS 2010)
  - Han Liu, Xi Chen
  - Paper  (https://papers.nips.cc/paper/4178-multivariate-dyadic-regression-trees-for-sparse-learning-problems.pdf)
  
- Fast and Accurate Gene Prediction by Decision Tree Classification (SDM 2010)
  - Rong She, Jeffrey Shih-Chieh Chu, Ke Wang, Nansheng Chen
  - Paper  (http://www.sfu.ca/~chenn/genBlastDT_sdm.pdf)

- A Robust Decision Tree Algorithm for Imbalanced Data Sets (SDM 2010)
  - Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla
  - Paper  (https://www3.nd.edu/~nchawla/papers/SDM10.pdf)

2009
- Stochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)
  - Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng
  - Paper  (https://dl.acm.org/citation.cfm?id=1646301)
  
- Feature Selection for Ranking Using Boosted Trees (CIKM 2009)
  - Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato
  - Paper  (http://www.francosalvetti.com/cikm09_camera2.pdf)
  
- Thai Word Segmentation with Hidden Markov Model and Decision Tree (PAKDD 2009)
  - Poramin Bheganan, Richi Nayak, Yue Xu
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-642-01307-2_10)

- Parameter Estimdation in Semi-Random Decision Tree Ensembling on Streaming Data (PAKDD 2009)
  - Pei-Pei Li, Qianhui Liang, Xindong Wu, Xuegang Hu
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-642-01307-2_35)

- DTU: A Decision Tree for Uncertain Data (PAKDD 2009)
  - Biao Qin, Yuni Xia, Fang Li
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-642-01307-2_4)

2008
- Predicting Future Decision Trees from Evolving Data (ICDM 2008)
  - Mirko Böttcher, Martin Spott, Rudolf Kruse
  - Paper  (https://ieeexplore.ieee.org/document/4781098)

- Bayes Optimal Classification for Decision Trees (ICML 2008)
  - Siegfried Nijssen
  - Paper  (http://icml2008.cs.helsinki.fi/papers/455.pdf)
  
- A New Credit Scoring Method Based on Rough Sets and Decision Tree (PAKDD 2008)
  - XiYue Zhou, Defu Zhang, Yi Jiang
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-540-68125-0_117)

- A Comparison of Different Off-Centered Entropies to Deal with Class Imbalance for Decision Trees (PAKDD 2008)
  - Philippe Lenca, Stéphane Lallich, Thanh-Nghi Do, Nguyen-Khang Pham
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-540-68125-0_59)

- BOAI: Fast Alternating Decision Tree Induction Based on Bottom-Up Evaluation (PAKDD 2008)
  - Bishan Yang, Tengjiao Wang, Dongqing Yang, Lei Chang
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-540-68125-0_36)

- A General Framework for Estimating Similarity of Datasets and Decision Trees: Exploring Semantic Similarity of Decision Trees (SDM 2008)
  - Irene Ntoutsi, Alexandros Kalousis, Yannis Theodoridis
  - Paper  (https://www.researchgate.net/publication/220907047_A_general_framework_for_estimating_similarity_of_datasets_and_decision_trees_exploring_semantic_similarity_of_decision_trees)

- ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data (SDM 2008)
  - M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
  - Paper  (https://pdfs.semanticscholar.org/bd80/db2f0903169b7611d34b2cc85f60a736375d.pdf)

2007

- Tree-based Classifiers for Bilayer Video Segmentation (CVPR 2007)
  - Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa
  - Paper  (https://ieeexplore.ieee.org/document/4270033)

- Additive Groves of Regression Trees (ECML 2007)
  - Daria Sorokina, Rich Caruana, Mirek Riedewald
  - Paper  (http://additivegroves.net/papers/groves.pdf)

- Decision Tree Instability and Active Learning (ECML 2007)
  - Kenneth Dwyer, Robert Holte
  - Paper  (https://webdocs.cs.ualberta.ca/~holte/Publications/ecml07.pdf)

- Ensembles of Multi-Objective Decision Trees (ECML 2007)
  - Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-540-74958-5_61)

- Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble (ECML 2007)
  - Anneleen Van Assche, Hendrik Blockeel
  - Paper  (http://ftp.cs.wisc.edu/machine-learning/shavlik-group/ilp07wip/ilp07_assche.pdf)

- Sample Compression Bounds for Decision Trees (ICML 2007)
  - Mohak Shah
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.9136&rep=rep1&type=pdf)

- A Tighter Error Bound for Decision Tree Learning Using PAC Learnability (IJCAI 2007)
  - Chaithanya Pichuka, Raju S. Bapi, Chakravarthy Bhagvati, Arun K. Pujari, Bulusu Lakshmana Deekshatulu
  - Paper  (https://www.ijcai.org/Proceedings/07/Papers/163.pdf)

- Keep the Decision Tree and Estimate the Class Probabilities Using its Decision Boundary (IJCAI 2007)
  - Isabelle Alvarez, Stephan Bernard, Guillaume Deffuant
  - Paper  (https://www.ijcai.org/Proceedings/07/Papers/104.pdf)

- Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)
  - Claudia Henry, Richard Nock, Frank Nielsen
  - Paper  (https://www.ijcai.org/Proceedings/07/Papers/135.pdf)

- Scalable Look-ahead Linear Regression Trees (KDD 2007)
  - David S. Vogel, Ognian Asparouhov, Tobias Scheffer
  - Paper  (https://www.cs.uni-potsdam.de/ml/publications/kdd2007.pdf)

- Mining Optimal Decision Trees from Itemset Lattices (KDD 2007)
  - Siegfried Nijssen, Élisa Fromont
  - Paper  (https://hal.archives-ouvertes.fr/hal-00372011/document)
  
- A Hybrid Multi-group Privacy-Preserving Approach for Building Decision Trees (PAKDD 2007)
  - Zhouxuan Teng, Wenliang Du
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-540-71701-0_30)

2006
- Decision Tree Methods for Finding Reusable MDP Homomorphisms (AAAI 2006)
  - Alicia P. Wolfe, Andrew G. Barto
  - Paper  (https://www.aaai.org/Papers/AAAI/2006/AAAI06-085.pdf)

- A Fast Decision Tree Learning Algorithm (AAAI 2006)
  - Jiang Su, Harry Zhang
  - Paper  (http://www.cs.unb.ca/~hzhang/publications/AAAI06.pdf)

- Anytime Induction of Decision Trees: An Iterative Improvement Approach (AAAI 2006)
  - Saher Esmeir, Shaul Markovitch
  - Paper  (https://www.aaai.org/Papers/AAAI/2006/AAAI06-056.pdf)

- When a Decision Tree Learner Has Plenty of Time (AAAI 2006)
  - Saher Esmeir, Shaul Markovitch
  - Paper  (https://www.aaai.org/Papers/AAAI/2006/AAAI06-259.pdf)

- Decision Trees for Functional Variables (ICDM 2006)
  - Suhrid Balakrishnan, David Madigan
  - Paper  (http://archive.dimacs.rutgers.edu/Research/MMS/PAPERS/fdt17.pdf)
  
- Cost-Sensitive Decision Tree Learning for Forensic Classification (ECML 2006)
  - Jason V. Davis, Jungwoo Ha, Christopher J. Rossbach, Hany E. Ramadan, Emmett Witchel
  - Paper  (https://www.cs.utexas.edu/users/witchel/pubs/davis-ecml06.pdf)

- Improving the Ranking Performance of Decision Trees (ECML 2006)
  - Bin Wang, Harry Zhang
  - Paper  (https://link.springer.com/chapter/10.1007/11871842_44)

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

- Variable Randomness in Decision Tree Ensembles (PAKDD 2006)
  - Fei Tony Liu, Kai Ming Ting
  - Paper  (https://link.springer.com/chapter/10.1007/11731139_12)

- Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees (PAKDD 2006)
  - Dan A. Simovici, Szymon Jaroszewicz
  - Paper  
(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-Metric-Splitti
ng-Criterion-for-Decision-Trees.pdf)

- Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics (PKDD 2006)
  - Hendrik Blockeel, Leander Schietgat, Jan Struyf, Saso Dzeroski, Amanda Clare
  - Paper  (https://link.springer.com/chapter/10.1007/11871637_7)

- k-Anonymous Decision Tree Induction (PKDD 2006)
  - Arik Friedman, Assaf Schuster, Ran Wolff
  - Paper  (http://www.cs.technion.ac.il/~arikf/online-publications/kADET06.pdf)

2005
- Representing Conditional Independence Using Decision Trees (AAAI 2005)
  - Jiang Su, Harry Zhang
  - Paper  (http://www.cs.unb.ca/~hzhang/publications/AAAI051SuJ.pdf)

- Use of Expert Knowledge for Decision Tree Pruning (AAAI 2005)
  - Jingfeng Cai, John Durkin
  - Paper  (http://www.aaai.org/Papers/AAAI/2005/SA05-009.pdf)
  
- Model Selection in Omnivariate Decision Trees (ECML 2005)
  - Olcay Taner Yildiz, Ethem Alpaydin
  - Paper  (https://www.cmpe.boun.edu.tr/~ethem/files/papers/yildiz_ecml05.pdf)

- Combining Bias and Variance Reduction Techniques for Regression Trees (ECML 2005)
  - Yuk Lai Suen, Prem Melville, Raymond J. Mooney
  - Paper  (http://www.cs.utexas.edu/users/ml/papers/bv-ecml-05.pdf)

- Simple Test Strategies for Cost-Sensitive Decision Trees (ECML 2005)
  - Shengli Sheng, Charles X. Ling, Qiang Yang
  - Paper  (https://www.researchgate.net/publication/3297582_Test_strategies_for_cost-sensitive_decision_trees)

- Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches (ICDM 2005)
  - Wei Fan, Ed Greengrass, Joe McCloskey, Philip S. Yu, Kevin Drummey
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.9713&rep=rep1&type=pdf)

- Exploiting Informative Priors for Bayesian Classification and Regression Trees (IJCAI 2005)
  - Nicos Angelopoulos, James Cussens
  - Paper  (https://www.ijcai.org/Proceedings/05/Papers/1013.pdf)

- Ranking Cases with Decision Trees: a Geometric Method that Preserves Intelligibility (IJCAI 2005)
  - Isabelle Alvarez, Stephan Bernard
  - Paper  (https://www.ijcai.org/Proceedings/05/Papers/1502.pdf)
  
- Maximizing Tree Diversity by Building Complete-Random Decision Trees (PAKDD 2005)
  - Fei Tony Liu, Kai Ming Ting, Wei Fan
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.7805&rep=rep1&type=pdf)

- Hybrid Cost-Sensitive Decision Tree (PKDD 2005)
  - Shengli Sheng, Charles X. Ling
  - Paper  (https://cling.csd.uwo.ca/papers/pkdd05a.pdf)

- Tree2 - Decision Trees for Tree Structured Data (PKDD 2005)
  - Björn Bringmann, Albrecht Zimmermann
  - Paper  (https://link.springer.com/chapter/10.1007/11564126_10)

- Building Decision Trees on Records Linked through Key References (SDM 2005)
  - Ke Wang, Yabo Xu, Philip S. Yu, Rong She
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.7181&rep=rep1&type=pdf)

- Decision Tree Induction in High Dimensional, Hierarchically Distributed Databases (SDM 2005)
  - Amir Bar-Or, Ran Wolff, Assaf Schuster, Daniel Keren
  - Paper  (https://www.semanticscholar.org/paper/Decision-Tree-Induction-in-High-Dimensional%2C-Bar-Or-Wolff/90235fc35c27dae273681f7847c2b20ff37928a9)

- Boosted Decision Trees for Word Recognition in Handwritten Document Retrieval (SIGIR 2005)
  - Nicholas R. Howe, Toni M. Rath, R. Manmatha
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.1551&rep=rep1&type=pdf)

2004
- On the Optimality of Probability Estimation by Random Decision Trees (AAAI 2004)
  - Wei Fan
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.447.2128&rep=rep1&type=pdf)

- Occam's Razor and a Non-Syntactic Measure of Decision Tree Complexity (AAAI 2004)
  - Goutam Paul
  - Paper  (https://www.aaai.org/Papers/AAAI/2004/AAAI04-130.pdf)

- Using Emerging Patterns and Decision Trees in Rare-Class Classification (ICDM 2004)
  - Hamad Alhammady, Kotagiri Ramamohanarao
  - Paper  (https://ieeexplore.ieee.org/abstract/document/1410299)

- Orthogonal Decision Trees (ICDM 2004)
  - Hillol Kargupta, Haimonti Dutta
  - Paper  (https://www.csee.umbc.edu/~hillol/PUBS/odtree.pdf)

- Improving the Reliability of Decision Tree and Naive Bayes Learners (ICDM 2004)
  - David George Lindsay, Siân Cox
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.521.3127&rep=rep1&type=pdf)

- Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data (ICDM 2004)
  - Chris Giannella, Kun Liu, Todd Olsen, Hillol Kargupta
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.7119&rep=rep1&type=pdf)

- Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams (ICDM 2004)
  - Wei Fan, Yi-an Huang, Philip S. Yu
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.9450&rep=rep1&type=pdf)

- Lookahead-based Algorithms for Anytime Induction of Decision Trees (ICML 2004)
  - Saher Esmeir, Shaul Markovitch
  - Paper  (http://www.cs.technion.ac.il/~shaulm/papers/pdf/Esmeir-Markovitch-icml2004.pdf)
  
- Decision Trees with Minimal Costs (ICML 2004)
  - Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang
  - Paper  (https://icml.cc/Conferences/2004/proceedings/papers/136.pdf)

- Training Conditional Random Fields via Gradient Tree Boosting (ICML 2004)
  - Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov
  - Paper  (http://web.engr.oregonstate.edu/~tgd/publications/ml2004-treecrf.pdf)

- Detecting Structural Metadata with Decision Trees and Transformation-Based Learning (NAACL 2004)
  - Joungbum Kim, Sarah E. Schwarm, Mari Ostendorf
  - Paper  (https://www.aclweb.org/anthology/N04-1018)

- On the Adaptive Properties of Decision Trees (NIPS 2004)
  - Clayton D. Scott, Robert D. Nowak
  - Paper  (https://papers.nips.cc/paper/2625-on-the-adaptive-properties-of-decision-trees.pdf)
  
- A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index (PAKDD 2004)
  - Dan A. Simovici, Szymon Jaroszewicz
  - Paper  (https://www.researchgate.net/publication/2906289_A_Metric_Approach_to_Building_Decision_Trees_Based_on_Goodman-Kruskal_Association_Index)

2003
- Rademacher Penalization over Decision Tree Prunings (ECML 2003)
  - Matti Kääriäinen, Tapio Elomaa
  - Paper  (https://www.researchgate.net/publication/221112653_Rademacher_Penalization_over_Decision_Tree_Prunings)
  
- Ensembles of Cascading Trees (ICDM 2003)
  - Jinyan Li, Huiqing Liu
  - Paper  (https://www.researchgate.net/publication/4047523_Ensembles_of_cascading_trees)

- Postprocessing Decision Trees to Extract Actionable Knowledge (ICDM 2003)
  - Qiang Yang, Jie Yin, Charles X. Ling, Tielin Chen
  - Paper  (https://pdfs.semanticscholar.org/b2c6/ff54c7aeefc70820ff04a8fc8b804012c504.pdf)

- K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier (ICDM 2003)
  - Tomoyuki Shibata, Takekazu Kato, Toshikazu Wada
  - Paper  (https://ieeexplore.ieee.org/abstract/document/1250997)

- Identifying Markov Blankets with Decision Tree Induction (ICDM 2003)
  - Lewis J. Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov
  - Paper  (https://www.semanticscholar.org/paper/Identifying-Markov-Blankets-with-Decision-Tree-Frey-Fisher/1aa0b0ede22f3963c923ea320a8bed91ac5aafbf)

- Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy (ICDM 2003)
  - Jin Huang, Jingjing Lu, Charles X. Ling
  - Paper  (https://pdfs.semanticscholar.org/8a73/74b98a9d94b8c01e996e72340f86a4327869.pdf)

- Boosting Lazy Decision Trees (ICML 2003)
  - Xiaoli Zhang Fern, Carla E. Brodley
  - Paper  (https://www.aaai.org/Papers/ICML/2003/ICML03-026.pdf)

- Decision Tree with Better Ranking (ICML 2003)
  - Charles X. Ling, Robert J. Yan
  - Paper  (https://www.aaai.org/Papers/ICML/2003/ICML03-064.pdf)

- Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction (IJCAI 2003)
  - David Page, Soumya Ray
  - Paper  (http://pages.cs.wisc.edu/~dpage/ijcai3.pdf)

- Efficient Decision Tree Construction on Streaming Data (KDD 2003)
  - Ruoming Jin, Gagan Agrawal
  - Paper  (http://web.cse.ohio-state.edu/~agrawal.28/p/sigkdd03.pdf)

- PaintingClass: Interactive Construction Visualization and Exploration of Decision Trees (KDD 2003)
  - Soon Tee Teoh, Kwan-Liu Ma
  - Paper  (https://www.researchgate.net/publication/220272011_PaintingClass_interactive_construction_visualization_and_exploration_of_decision_trees)

- Accurate Decision Trees for Mining High-Speed Data Streams (KDD 2003)
  - João Gama, Ricardo Rocha, Pedro Medas
  - Paper  (http://staff.icar.cnr.it/manco/Teaching/2006/datamining/Esami2006/ArticoliSelezionatiDM/SEMINARI/Mining%20Data%20Streams/kdd03.pdf)

- Near-Minimax Optimal Classification with Dyadic Classification Trees (NIPS 2003)
  - Clayton D. Scott, Robert D. Nowak
  - Paper  (http://nowak.ece.wisc.edu/nips03.pdf)
  
- Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule (PAKDD 2003)
  - Chenzhou Ye, Jie Yang, Lixiu Yao, Nian-yi Chen
  - Paper  (https://www.researchgate.net/publication/220895462_Improving_Performance_of_Decision_Tree_Algorithms_with_Multi-edited_Nearest_Neighbor_Rule)

- Arbogodai: a New Approach for Decision Trees (PKDD 2003)
  - Djamel A. Zighed, Gilbert Ritschard, Walid Erray, Vasile-Marian Scuturici
  - Paper  (http://mephisto.unige.ch/pub/publications/gr/zig_rit_arbo_pkdd03.pdf)

- Communication and Memory Efficient Parallel Decision Tree Construction (SDM 2003)
  - Ruoming Jin, Gagan Agrawal
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.3059&rep=rep1&type=pdf)

- Decision Tree Classification of Spatial Data Patterns from Videokeratography using Zernicke Polynomials (SDM 2003)
  - Michael D. Twa, Srinivasan Parthasarathy, Thomas W. Raasch, Mark Bullimore
  - Paper  (https://www.researchgate.net/publication/220907147_Decision_Tree_Classification_of_Spatial_Data_Patterns_From_Videokeratography_Using_Zernike_Polynomials)

2002

- Multiclass Alternating Decision Trees (ECML 2002)
  - Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark A. Hall
  - Paper  (https://www.cs.waikato.ac.nz/~bernhard/papers/ecml2002.pdf)
  
- Heterogeneous Forests of Decision Trees (ICANN 2002)
  - Krzysztof Grabczewski, Wlodzislaw Duch
  - Paper  (https://fizyka.umk.pl/publications/kmk/02forest.pdf)

- Solving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)
  - Jinyan Li, Limsoon Wong
  - Paper  (https://ieeexplore.ieee.org/document/1184021)

- Solving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)
  - Jinyan Li, Limsoon Wong
  - Paper  (https://www.comp.nus.edu.sg/~wongls/psZ/decisionTreeandEP-2.ps)

- Learning Decision Trees Using the Area Under the ROC Curve (ICML 2002)
  - César Ferri, Peter A. Flach, José Hernández-Orallo
  - Paper  (http://dmip.webs.upv.es/papers/ICML2002.pdf)

- Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction (ICML 2002)
  - Fumio Takechi, Einoshin Suzuki
  - Paper  (https://www.researchgate.net/publication/221346121_Finding_an_Optimal_Gain-Ratio_Subset-Split_Test_for_a_Set-Valued_Attribute_in_Decision_Tree_Induction)

- Efficiently Mining Frequent Trees in a Forest (KDD 2002)
  - Mohammed Javeed Zaki
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.8511&rep=rep1&type=pdf)

- SECRET: a Scalable Linear Regression Tree Algorithm (KDD 2002)
  - Alin Dobra, Johannes Gehrke
  - Paper  (http://www.cs.cornell.edu/people/dobra/papers/secret-extended.pdf)

- Instability of Decision Tree Classification Algorithms (KDD 2002)
  - Ruey-Hsia Li, Geneva G. Belford
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.8094&rep=rep1&type=pdf)

- Extracting Decision Trees From Trained Neural Networks (KDD 2002)
  - Olcay Boz
  - Paper  (http://dspace.library.iitb.ac.in/jspui/bitstream/10054/1285/1/5664.pdf)

- Dyadic Classification Trees via Structural Risk Minimization (NIPS 2002)
  - Clayton D. Scott, Robert D. Nowak
  - Paper  (https://papers.nips.cc/paper/2198-dyadic-classification-trees-via-structural-risk-minimization.pdf)
  
- Approximate Splitting for Ensembles of Trees using Histograms (SDM 2002)
  - Chandrika Kamath, Erick Cantú-Paz, David Littau
  - Paper  (https://pdfs.semanticscholar.org/0855/0a94993a268e4e3e99c41e7e0ee43eabd993.pdf)

2001
- Japanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning (ACL 2001)
  - Hideki Isozaki
  - Paper  (https://www.aclweb.org/anthology/P01-1041)

- Message Length as an Effective Ockham's Razor in Decision Tree Induction (AISTATS 2001)
  - Scott Needham, David L. Dowe
  - Paper  (www.gatsby.ucl.ac.uk/aistats/aistats2001/files/needham122.ps)

- SQL Database Primitives for Decision Tree Classifiers (CIKM 2001)
  - Kai-Uwe Sattler, Oliver Dunemann
  - Paper  (http://fusion.cs.uni-magdeburg.de/pubs/classprim.pdf)
  
- A Unified Framework for Evaluation Metrics in Classification Using Decision Trees (ECML 2001)
  - Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish
  - Paper  (https://scholar.harvard.edu/files/nkc/files/2015_framework_for_benefit_risk_assessment_value_in_health.pdf)

- Backpropagation in Decision Trees for Regression (ECML 2001)
  - Victor Medina-Chico, Alberto Suárez, James F. Lutsko
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-44795-4_30)

- Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction (ECML 2001)
  - Branko Kavsek, Nada Lavrac, Anuska Ferligoj
  - Paper  (https://link.springer.com/content/pdf/10.1007/3-540-44795-4_22.pdf)

- Mining Decision Trees from Data Streams in a Mobile Environment (ICDM 2001)
  - Hillol Kargupta, Byung-Hoon Park
  - Paper  (https://ieeexplore.ieee.org/document/989530)

- Efficient Determination of Dynamic Split Points in a Decision Tree (ICDM 2001)
  - David Maxwell Chickering, Christopher Meek, Robert Rounthwaite
  - Paper  (https://pdfs.semanticscholar.org/3587/a245c34ea415b205a903bde3220eb533d1a7.pdf)

- A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)
  - Bernard Zenko, Ljupco Todorovski, Saso Dzeroski
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.3118&rep=rep1&type=pdf)

- Efficient Algorithms for Decision Tree Cross-Validation (ICML 2001)
  - Hendrik Blockeel, Jan Struyf
  - Paper  (http://www.jmlr.org/papers/volume3/blockeel02a/blockeel02a.pdf)

- Bias Correction in Classification Tree Construction (ICML 2001)
  - Alin Dobra, Johannes Gehrke
  - Paper  (http://www.cs.cornell.edu/people/dobra/papers/icml2001-bias.pdf)

- Breeding Decision Trees Using Evolutionary Techniques (ICML 2001)
  - Athanassios Papagelis, Dimitrios Kalles
  - Paper  (http://www.gatree.com/data/BreedinDecisioTreeUsinEvo.pdf)

- Obtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers (ICML 2001)
  - Bianca Zadrozny, Charles Elkan
  - Paper  (http://cseweb.ucsd.edu/~elkan/calibrated.pdf)

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

- A Decision Tree of Bigrams is an Accurate Predictor of Word Sense (NAACL 2001)
  - Ted Pedersen
  - Paper  (https://www.aclweb.org/anthology/N01-1011)
  
- Rule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron (PAKDD 2001)
  - DaeEun Kim, Jaeho Lee
  - Paper  (https://dl.acm.org/citation.cfm?id=693490)

- A Scalable Algorithm for Rule Post-pruning of Large Decision Trees (PAKDD 2001)
  - Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-45357-1_49)

- Optimizing the Induction of Alternating Decision Trees (PAKDD 2001)
  - Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby
  - Paper  (https://www.researchgate.net/publication/33051701_Optimizing_the_Induction_of_Alternating_Decision_Trees)

- Interactive Construction of Decision Trees (PAKDD 2001)
  - Jianchao Han, Nick Cercone
  - Paper  (https://pure.tue.nl/ws/files/3522084/672434611234867.pdf)

- Bloomy Decision Tree for Multi-objective Classification (PKDD 2001)
  - Einoshin Suzuki, Masafumi Gotoh, Yuta Choki
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-44794-6_36)
  
- A Fourier Analysis Based Approach to Learning Decision Trees in a Distributed Environment (SDM 2001)
  - Byung-Hoon Park, Rajeev Ayyagari, Hillol Kargupta
  - Paper  (https://archive.siam.org/meetings/sdm01/pdf/sdm01_19.pdf)
  
2000

- Intuitive Representation of Decision Trees Using General Rules and Exceptions (AAAI 2000)
  - Bing Liu, Minqing Hu, Wynne Hsu
  - Paper  (https://pdfs.semanticscholar.org/e284/96551e595f1850a53f93affa98919147712f.pdf)

- Tagging Unknown Proper Names Using Decision Trees (ACL 2000)
  - Frédéric Béchet, Alexis Nasr, Franck Genet
  - Paper  (https://www.aclweb.org/anthology/P00-1011)

- Clustering Through Decision Tree Construction (CIKM 2000)
  - Bing Liu, Yiyuan Xia, Philip S. Yu
  - Paper  (https://dl.acm.org/citation.cfm?id=354775)

- Handling Continuous-Valued Attributes in Decision Tree with Neural Network Modelling (ECML 2000)
  - DaeEun Kim, Jaeho Lee
  - Paper  (https://link.springer.com/content/pdf/10.1007/3-540-45164-1_22.pdf)

- Investigation and Reduction of Discretization Variance in Decision Tree Induction (ECML 2000)
  - Pierre Geurts, Louis Wehenkel
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-45164-1_17)

- Nonparametric Regularization of Decision Trees (ECML 2000)
  - Tobias Scheffer
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-45164-1_36)

- Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria (ICML 2000)
  - Chris Drummond, Robert C. Holte
  - Paper  (https://pdfs.semanticscholar.org/160e/21c3acc925b60dc040cb1705e58bb166b045.pdf)

- Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (ICML 2000)
  - Manu Sridharan, Gerald Tesauro
  - Paper  (https://manu.sridharan.net/files/icml00.pdf)

- Growing Decision Trees on Support-less Association Rules (KDD 2000)
  - Ke Wang, Senqiang Zhou, Yu He
  - Paper  (https://www2.cs.sfu.ca/~wangk/pub/kdd002.pdf)

- Efficient Algorithms for Constructing Decision Trees with Constraints (KDD 2000)
  - Minos N. Garofalakis, Dongjoon Hyun, Rajeev Rastogi, Kyuseok Shim
  - Paper  (http://www.softnet.tuc.gr/~minos/Papers/kdd00-cam.pdf)

- Interactive Visualization in Mining Large Decision Trees (PAKDD 2000)
  - Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira
  - Paper  (https://link.springer.com/content/pdf/10.1007/3-540-45571-X_40.pdf)

- VQTree: Vector Quantization for Decision Tree Induction (PAKDD 2000)
  - Shlomo Geva, Lawrence Buckingham
  - Paper  (https://link.springer.com/chapter/10.1007%2F3-540-45571-X_41)

- Some Enhencements of Decision Tree Bagging (PKDD 2000)
  - Pierre Geurts
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-45372-5_14)

- Combining Multiple Models with Meta Decision Trees (PKDD 2000)
  - Ljupco Todorovski, Saso Dzeroski
  - Paper  (http://kt.ijs.si/bernard/mdts/pub01.pdf)

- Induction of Multivariate Decision Trees by Using Dipolar Criteria (PKDD 2000)
  - Leon Bobrowski, Marek Kretowski
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-45372-5_33)

- Decision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms (PKDD 2000)
  - Nikos Drossos, Athanassios Papagelis, Dimitrios Kalles
  - Paper  (https://link.springer.com/chapter/10.1007/3-540-45372-5_40)

1999
- Modeling Decision Tree Performance with the Power Law (AISTATS 1999)
  - Lewis J. Frey, Douglas H. Fisher
  - Paper  (https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/ModelingTree.pdf)

- Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens (AISTATS 1999)
  - Louis Anthony Cox Jr.
  - Paper  (https://pdfs.semanticscholar.org/0d7b/1d55c5abfd024aacf645c66d0c90c283814e.pdf)

- POS Tags and Decision Trees for Language Modeling (EMNLP 1999)
  - Peter A. Heeman
  - Paper  (https://www.aclweb.org/anthology/W99-0617)

- Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)
  - Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
  - Paper  (https://pdfs.semanticscholar.org/067e/86836ddbcb5e2844e955c16e058366a18c77.pdf)

- The Alternating Decision Tree Learning Algorithm (ICML 1999)
  - Yoav Freund, Llew Mason
  - Paper  (https://cseweb.ucsd.edu/~yfreund/papers/atrees.pdf)
  - Code  (https://github.com/rajanil/mkboost)

- Boosting with Multi-Way Branching in Decision Trees (NIPS 1999)
  - Yishay Mansour, David A. McAllester
  - Paper  (https://papers.nips.cc/paper/1659-boosting-with-multi-way-branching-in-decision-trees.pdf)

1998
- Learning Sorting and Decision Trees with POMDPs (ICML 1998)
  - Blai Bonet, Hector Geffner
  - Paper  (https://bonetblai.github.io/reports/icml98-learning.pdf)

- Using a Permutation Test for Attribute Selection in Decision Trees (ICML 1998)
  - Eibe Frank, Ian H. Witten
  - Paper  (https://pdfs.semanticscholar.org/9aa9/21b0203e06e98b49bf726a33e124f4310ea3.pdf)

- A Fast and Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization (ICML 1998)
  - Michael J. Kearns, Yishay Mansour
  - Paper  (https://www.cis.upenn.edu/~mkearns/papers/pruning.pdf)

1997
- Pessimistic Decision Tree Pruning Based Continuous-Time (ICML 1997)
  - Yishay Mansour
  - Paper  (https://pdfs.semanticscholar.org/b6fc/e37612db10a9756b904b5e79e1144ca12574.pdf)

- PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (ICML 1997)
  - Scott E. Decatur
  - Paper  (https://www.semanticscholar.org/paper/PAC-Learning-with-Constant-Partition-Classification-Decatur/dd205073aeb512ecd1e823b35f556058fdeea5e0)

- Option Decision Trees with Majority Votes (ICML 1997)
  - Ron Kohavi, Clayton Kunz
  - Paper  (https://pdfs.semanticscholar.org/383b/381d1ac0bb41ec595e0d1603ed642809eb86.pdf)

- Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction (ICML 1997)
  - Ricardo Vilalta, Larry A. Rendell
  - Paper  (https://pdfs.semanticscholar.org/1f73/d9d409a75d16871cfa1182ac72b37c839d86.pdf)

- Functional Models for Regression Tree Leaves (ICML 1997)
  - Luís Torgo
  - Paper  (https://pdfs.semanticscholar.org/48e4/b3187ca234308e97e1ac0cab84222c603bdd.pdf)

- The Effects of Training Set Size on Decision Tree Complexity (ICML 1997)
  - Tim Oates, David D. Jensen
  - Paper  (https://pdfs.semanticscholar.org/e003/9dbdec3bd4cfbb3273b623fbed2d6b2f0cc9.pdf)

- Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis (NIPS 1997)
  - Marcus Held, Joachim M. Buhmann
  - Paper  (https://papers.nips.cc/paper/1479-unsupervised-on-line-learning-of-decision-trees-for-hierarchical-data-analysis.pdf)

- Data-Dependent Structural Risk Minimization for Perceptron Decision Trees (NIPS 1997)
  - John Shawe-Taylor, Nello Cristianini
  - Paper  (https://papers.nips.cc/paper/1359-data-dependent-structural-risk-minimization-for-perceptron-decision-trees)

- Generalization in Decision Trees and DNF: Does Size Matter (NIPS 1997)
  - Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason
  - Paper  (https://papers.nips.cc/paper/1340-generalization-in-decision-trees-and-dnf-does-size-matter.pdf)

1996
- Second Tier for Decision Trees (ICML 1996)
  - Miroslav Kubat
  - Paper  (https://pdfs.semanticscholar.org/b619/7c531b1c83dfaa52563449f9b8248cc68c5a.pdf)

- Non-Linear Decision Trees - NDT (ICML 1996)
  - Andreas Ittner, Michael Schlosser
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.85.2133&rep=rep1&type=pdf)

- Learning Relational Concepts with Decision Trees (ICML 1996)
  - Peter Geibel, Fritz Wysotzki
  - Paper  (https://pdfs.semanticscholar.org/32f1/78d7266fee779257b87ac8f948951db57d1e.pdf)

1995
- A Hill-Climbing Approach for Optimizing Classification Trees (AISTATS 1995)
  - Xiaorong Sun, Steve Y. Chiu, Louis Anthony Cox Jr.
  - Paper  (https://link.springer.com/chapter/10.1007%2F978-1-4612-2404-4_11)

- An Exact Probability Metric for Decision Tree Splitting (AISTATS 1995)
  - J. Kent Martin
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.48.6378&rep=rep1&type=pdf)

- On Pruning and Averaging Decision Trees (ICML 1995)
  - Jonathan J. Oliver, David J. Hand
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.6733&rep=rep1&type=pdf)

- On Handling Tree-Structured Attributed in Decision Tree Learning (ICML 1995)
  - Hussein Almuallim, Yasuhiro Akiba, Shigeo Kaneda
  - Paper  (https://www.sciencedirect.com/science/article/pii/B9781558603776500116)

- Retrofitting Decision Tree Classifiers Using Kernel Density Estimation (ICML 1995)
  - Padhraic Smyth, Alexander G. Gray, Usama M. Fayyad
  - Paper  (https://pdfs.semanticscholar.org/3a05/8ab505f096b23962591bb14e495a543aa2a1.pdf)

- Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves (ICML 1995)
  - David J. Lubinsky
  - Paper  (https://www.sciencedirect.com/science/article/pii/B9781558603776500530)

- Efficient Algorithms for Finding Multi-way Splits for Decision Trees (ICML 1995)
  - Truxton Fulton, Simon Kasif, Steven Salzberg
  - Paper  (https://www.sciencedirect.com/science/article/pii/B9781558603776500384)

- Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (ICML 1995)
  - Peter Auer, Robert C. Holte, Wolfgang Maass
  - Paper  (https://igi-web.tugraz.at/PDF/77.pdf)

- Boosting Decision Trees (NIPS 1995)
  - Harris Drucker, Corinna Cortes
  - Paper  (http://papers.nips.cc/paper/1059-boosting-decision-trees.pdf)

- Using Pairs of Data-Points to Define Splits for Decision Trees (NIPS 1995)
  - Geoffrey E. Hinton, Michael Revow
  - Paper  (https://www.cs.toronto.edu/~hinton/absps/bcart.pdf)

- A New Pruning Method for Solving Decision Trees and Game Trees (UAI 1995)
  - Prakash P. Shenoy
  - Paper  (https://arxiv.org/abs/1302.4981)

1994
- A Statistical Approach to Decision Tree Modeling (ICML 1994)
  - Michael I. Jordan
  - Paper  (https://www.sciencedirect.com/science/article/pii/B9781558603356500519)

- In Defense of C4.5: Notes Learning One-Level Decision Trees (ICML 1994)
  - Tapio Elomaa
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.9386)

- An Improved Algorithm for Incremental Induction of Decision Trees (ICML 1994)
  - Paul E. Utgoff
  - Paper  (https://www.sciencedirect.com/science/article/pii/B9781558603356500465)
  
- Decision Tree Parsing using a Hidden Derivation Model (NAACL 1994)
  - Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Adwait Ratnaparkhi, Salim Roukos
  - Paper  (http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/H/H94/H94-1052.pdf)

1993
- Using Decision Trees to Improve Case-Based Learning (ICML 1993)
  - Claire Cardie
  - Paper  (https://www.cs.cornell.edu/home/cardie/papers/ml-93.ps)
  
1991
- Context Dependent Modeling of Phones in Continuous Speech Using Decision Trees (NAACL 1991)
  - Lalit R. Bahl, Peter V. de Souza, P. S. Gopalakrishnan, David Nahamoo, Michael Picheny
  - Paper  (https://www.aclweb.org/anthology/H91-1051.pdf)

1989
- Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications (NIPS 1989)
  - Les E. Atlas, Ronald A. Cole, Jerome T. Connor, Mohamed A. El-Sharkawi, Robert J. Marks II, Yeshwant K. Muthusamy, Etienne Barnard
  - Paper  (https://papers.nips.cc/paper/203-performance-comparisons-between-backpropagation-networks-and-classification-trees-on-three-real-world-applications)

1988
- Multiple Decision Trees (UAI 1988)
  - Suk Wah Kwok, Chris Carter
  - Paper  (https://arxiv.org/abs/1304.2363)

1987
- Decision Tree Induction Systems: A Bayesian Analysis (UAI 1987)
  - Wray L. Buntine
  - Paper  (https://arxiv.org/abs/1304.2732)
  
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License

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