# Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage

arXiv: https://arxiv.org/abs/2004.10931 · HTML: https://mlsanity.com/paper/2004.10931/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Partitioned Active Learning for Heterogeneous Systems](https://mlsanity.com/paper/2105.08547/) (2021)
- [Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels](https://mlsanity.com/paper/2306.09819/) (2023)
- [A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems](https://mlsanity.com/paper/2109.10898/) (2021)
- [Failure-averse Active Learning for Physics-constrained Systems](https://mlsanity.com/paper/2110.14443/) (2021)
- [Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems](https://mlsanity.com/paper/2303.10022/) (2023)
- [Geometry-aware Active Learning of Spatiotemporal Dynamic Systems](https://mlsanity.com/paper/2504.19012/) (2025)
- [Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes](https://mlsanity.com/paper/2312.03176/) (2023)
- [Knowledge Modelling and Active Learning in Manufacturing](https://mlsanity.com/paper/2107.02298/) (2021)
- [Applied metamodelling for ATM performance simulations](https://mlsanity.com/paper/2308.03404/) (2023)
- [Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization](https://mlsanity.com/paper/2309.02818/) (2023)
- [Amortized Active Learning for Nonparametric Functions](https://mlsanity.com/paper/2407.17992/) (2024)

## Abstract (arXiv)

In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for the industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression such as linear regression or Gaussian process. Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for Gaussian process. In this paper, we propose two new active learning algorithms for the Gaussian process with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact from uncertainties, and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage.