# Improved active output selection strategy for noisy environments

arXiv: https://arxiv.org/abs/2101.03499 · HTML: https://mlsanity.com/paper/2101.03499/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Near-Optimal Active Learning of Multi-Output Gaussian Processes](https://mlsanity.com/paper/1511.06891/) (2015)
- [Active Output Selection Strategies for Multiple Learning Regression Models](https://mlsanity.com/paper/2011.14307/) (2020)

## Abstract (arXiv)

The test bench time needed for model-based calibration can be reduced with active learning methods for test design. This paper presents an improved strategy for active output selection. This is the task of learning multiple models in the same input dimensions and suits the needs of calibration tasks. Compared to an existing strategy, we take into account the noise estimate, which is inherent to Gaussian processes. The method is validated on three different toy examples. The performance compared to the existing best strategy is the same or better in each example. In a best case scenario, the new strategy needs at least 10% less measurements compared to all other active or passive strategies. Further efforts will evaluate the strategy on a real-world application. Moreover, the implementation of more sophisticated active-learning strategies for the query placement will be realized.