Gradient Boosting Machine: A Survey
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Abstract (arXiv)
In this survey, we discuss several different types of gradient boosting algorithms and illustrate their mathematical frameworks in detail: 1. introduction of gradient boosting leads to 2. objective function optimization, 3. loss function estimations, and 4. model constructions. 5. application of boosting in ranking.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Aug 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2023Teaching Language Models to Self-Improve through Interactive Demonstrationscited by 26
- 2023Trustworthy AI: Deciding What to Decidecited by 3
- 2023Interpretable Ensembles of Hyper-Rectangles as Base Modelscited by 2
- 2025Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processingcited by 1
- 2021To Boost or not to Boost: On the Limits of Boosted Neural Networkscited by 0
- 2025A Small Dataset May Go a Long Way: Process Duration Prediction in Clinical Settingscited by 0
- 2026From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworkscited by 0
- 2026From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks20 Jun 2026
- 2025A Small Dataset May Go a Long Way: Process Duration Prediction in Clinical Settings19 Aug 2025
- 2025Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processing9 Jan 2025
- 2023Trustworthy AI: Deciding What to Decide21 Nov 2023
- 2023Teaching Language Models to Self-Improve through Interactive Demonstrations20 Oct 2023
- 2023Interpretable Ensembles of Hyper-Rectangles as Base Models15 Mar 2023
- 2021To Boost or not to Boost: On the Limits of Boosted Neural Networks28 Jul 2021