# Ensemble-based Hybrid Optimization of Bayesian Neural Networks and Traditional Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2310.05456 · HTML: https://mlsanity.com/paper/2310.05456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [NGBoost: Natural Gradient Boosting for Probabilistic Prediction](https://mlsanity.com/paper/1910.03225/) (2019)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Adaptive Restart of the Optimized Gradient Method for Convex Optimization](https://mlsanity.com/paper/1703.04641/) (2017)
- [Bayesian Neural Networks With Maximum Mean Discrepancy Regularization](https://mlsanity.com/paper/2003.00952/) (2020)

## Abstract (arXiv)

This research introduces a novel methodology for optimizing Bayesian Neural Networks (BNNs) by synergistically integrating them with traditional machine learning algorithms such as Random Forests (RF), Gradient Boosting (GB), and Support Vector Machines (SVM). Feature integration solidifies these results by emphasizing the second-order conditions for optimality, including stationarity and positive definiteness of the Hessian matrix. Conversely, hyperparameter tuning indicates a subdued impact in improving Expected Improvement (EI), represented by EI(x). Overall, the ensemble method stands out as a robust, algorithmically optimized approach.