# Bayesian Active Learning for Structured Output Design

arXiv: https://arxiv.org/abs/1911.03671 · HTML: https://mlsanity.com/paper/1911.03671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

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

- [Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology](https://mlsanity.com/paper/2107.05499/) (2021)
- [Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions](https://mlsanity.com/paper/2305.02663/) (2023)

## Abstract (arXiv)

In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output. The proposed method provides new acquisition functions for minimizing the error between the desired structured-output and the prediction of a Gaussian process model, by effectively incorporating the correlation between multiple outputs of the underlying multi-valued black box output functions. The effectiveness of the proposed method is verified by applying it to two synthetic shape search problem and real data. In the real data experiment, we tackle the input parameter search which achieves the desired crystal growth rate in silicon carbide (SiC) crystal growth modeling, that is a problem of materials informatics.