# Weighted Ensembles for Active Learning with Adaptivity

arXiv: https://arxiv.org/abs/2206.05009 · HTML: https://mlsanity.com/paper/2206.05009/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Structure Discovery in Nonparametric Regression through Compositional Kernel Search](https://mlsanity.com/paper/1302.4922/) (2013)
- [Active Learning in Robotics: A Review of Control Principles](https://mlsanity.com/paper/2106.13697/) (2021)
- [Conditioning Sparse Variational Gaussian Processes for Online Decision-making](https://mlsanity.com/paper/2110.15172/) (2021)
- [Surrogate modeling for Bayesian optimization beyond a single Gaussian process](https://mlsanity.com/paper/2205.14090/) (2022)
- [Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes](https://mlsanity.com/paper/1706.02524/) (2017)
- [Incremental Ensemble Gaussian Processes](https://mlsanity.com/paper/2110.06777/) (2021)
- [An Entropy Search Portfolio for Bayesian Optimization](https://mlsanity.com/paper/1406.4625/) (2014)
- [Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression](https://mlsanity.com/paper/1912.02641/) (2019)
- [Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes](https://mlsanity.com/paper/2112.00882/) (2021)

## Built on by (most cited first, 2 of 2)

- [3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning](https://mlsanity.com/paper/2310.00145/) (2023)
- [Adaptive Bayesian Optimization for Robust Identification of Stochastic Dynamical Systems](https://mlsanity.com/paper/2503.06381/) (2025)

## Abstract (arXiv)

Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects the most informative data instances to label on-the-fly. This active sampling process can benefit from a statistical function model, that is typically captured by a Gaussian process (GP). While most GP-based AL approaches rely on a single kernel function, the present contribution advocates an ensemble of GP models with weights adapted to the labeled data collected incrementally. Building on this novel EGP model, a suite of acquisition functions emerges based on the uncertainty and disagreement rules. An adaptively weighted ensemble of EGP-based acquisition functions is also introduced to further robustify performance. Extensive tests on synthetic and real datasets showcase the merits of the proposed EGP-based approaches with respect to the single GP-based AL alternatives.