Dynamic Meta-Learning for Adaptive XGBoost-Neural Ensembles
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Abstract (arXiv)
This paper introduces a novel adaptive ensemble framework that synergistically combines XGBoost and neural networks through sophisticated meta-learning. The proposed method leverages advanced uncertainty quantification techniques and feature importance integration to dynamically orchestrate model selection and combination. Experimental results demonstrate superior predictive performance and enhanced interpretability across diverse datasets, contributing to the development of more intelligent and flexible machine learning systems.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 30 Sep 2025.
Builds on
11 of 32 references.
References · 11
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2017Axiomatic Attribution for Deep Networkscited by 3,350
- 2017Prototypical Networks for Few-shot Learningcited by 3,001
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
- 2014Deep Learning in Neural Networks: An Overviewcited by 765
- 2015Understanding Neural Networks Through Deep Visualizationcited by 332
- 2018Meta-Learning: A Surveycited by 254
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Citing papers per year: 2026: 1.
1 paper.
- 2026Regularized Meta-Learning for Improved Generalizationcited by 0
- 2026Regularized Meta-Learning for Improved Generalization12 Feb 2026