Awesome Gradient Boosting Research Papers

<p align="center"> <img width="450" src="boosting.gif"> </p>

---

A curated list of gradient and adaptive boosting papers with implementations from the following conferences:

• Machine learning
  • NeurIPS
  • ICML
  • ICLR
• Computer vision
  • CVPR
  • ICCV
  • ECCV
• Natural language processing
  • ACL
  • NAACL
  • EMNLP
• Data
  • KDD
  • CIKM
  • ICDM
  • SDM
  • PAKDD
  • PKDD/ECML
  • RECSYS
  • SIGIR
  • WWW
  • WSDM
• Artificial intelligence
  • AAAI
  • AISTATS
  • ICANN
  • IJCAI
  • UAI

Similar collections about graph classification, classification/regression tree, fraud detection, Monte Carlo tree search, and community detection papers with implementations.

2025

• Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles (AAAI 2025)

  • Youssouf Emine, Alexandre Forel, Idriss Malek, Thibaut Vidal
  • [Paper]
  • [Code]

• Supervised Score-Based Modeling by Gradient Boosting (AAAI 2025)

  • Changyuan Zhao, Hongyang Du, Guangyuan Liu, Dusit Niyato
  • [Paper]

• Additive Model Boosting: New Insights and Pathologies (AISTATS 2025)

  • Rickmer Schulte, David Rügamer
  • [Paper]

• FairRegBoost: An End-to-End Data Processing Framework for Fair and Scalable Regression (CIKM 2025)

  • Nico Lässig, Melanie Herschel
  • [Paper]
  • [Code]

• Federated Gradient Boosting for Financial Fraud Detection: An Empirical Study in the Banking Sector (CIKM 2025)

  • Dae-Young Park, In-Young Ko, Taek-Ho Lee, Junghye Lee
  • [Paper]

• Boosting Methods for Interval-censored Data with Regression and Classification (ICLR 2025)

  • Yuan Bian, Grace Y. Yi, Wenqing He
  • [Paper]
  • [Code]

• NRGBoost: Energy-Based Generative Boosted Trees (ICLR 2025)

  • João Bravo
  • [Paper]
  • [Code]

• Gradient Boosting Reinforcement Learning (ICML 2025)

  • Benjamin Fuhrer, Chen Tessler, Gal Dalal
  • [Paper]
  • [Code]

• Fast Calculation of Feature Contributions in Boosting Trees (UAI 2025)

  • Zhongli Jiang, Min Zhang, Dabao Zhang
  • [Paper]

• Learning Robust XGBoost Ensembles for Regression Tasks (UAI 2025)

  • Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio
  • [Paper]

2024

• Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles (AISTATS 2024)

  • Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley
  • [Paper]
  • [Code]

• Distributed Boosting: An Enhancing Method on Dataset Distillation (CIKM 2024)

  • Xuechao Chen, Wenchao Meng, Peiran Wang, Qihang Zhou
  • [Paper]

• Adversarial Imitation Learning via Boosting (ICLR 2024)

  • Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun
  • [Paper]

• Iterative Weak Learnability and Multiclass AdaBoost (KDD 2024)

  • In-Koo Cho, Jonathan A. Libgober, Cheng Ding
  • [Paper]

• Uplift Modelling via Gradient Boosting (KDD 2024)

  • Bulat Ibragimov, Anton Vakhrushev
  • [Paper]

• AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation (KDD 2024)

  • Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang
  • [Paper]
  • [Code]

• PEMBOT: Pareto-Ensembled Multi-task Boosted Trees (KDD 2024)

  • Gokul Swamy, Anoop Saladi, Arunita Das, Shobhit Niranjan
  • [Paper]

2023

• Computing Abductive Explanations for Boosted Trees (AISTATS 2023)

  • Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski
  • [Paper]

• Boosted Off-Policy Learning (AISTATS 2023)

  • Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
  • [Paper]

• Variational Boosted Soft Trees (AISTATS 2023)

  • Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov
  • [Paper]

• Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces (AISTATS 2023)

  • Eric Xia, Martin J. Wainwright
  • [Paper]

• FairGBM: Gradient Boosting with Fairness Constraints (ICLR 2023)

  • André Ferreira Cruz, Catarina Belém, João Bravo, Pedro Saleiro, Pedro Bizarro
  • [Paper]

• Gradient Boosting Performs Gaussian Process Inference (ICLR 2023)

  • Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
  • [Paper]

2022

• 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]

• A Resilient Distributed Boosting Algorithm (ICML 2022)

  • Yuval Filmus, Idan Mehalel, Shay Moran
  • [Paper]

• Fast Provably Robust Decision Trees and Boosting (ICML 2022)

  • Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou
  • [Paper]

• Building Robust Ensembles via Margin Boosting (ICML 2022)

  • Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
  • [Paper]

• 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]

• Federated Functional Gradient Boosting (AISTATS 2022)

  • Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi
  • [Paper]

• ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics (AISTATS 2022)

  • Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein
  • [Paper]

2021

• Precision-based Boosting (AAAI 2021)

  • Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles
  • [Paper]

• BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data (CIKM 2021)

  • Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach
  • [Paper]

• Unsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees (CIKM 2021)

  • Panpan Qi, Wei Wang, Lei Zhu, See-Kiong Ng
  • [Paper]

• Individually Fair Gradient Boosting (ICLR 2021)

  • Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun
  • [Paper]

• 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]

• AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models (ICLR 2021)

  • Ke Sun, Zhanxing Zhu, Zhouchen Lin
  • [[Paper]](https://arxiv.org/abs/1908.0508