Title: Bayesian Active Learning for Structured Output Design Show Chinese title

Title: 基于结构化输出设计的贝叶斯主动学习

Abstract: 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. Show Chinese abstract

Abstract: 在本文中，我们提出了一种用于逆问题的主动学习方法，旨在找到一个输入以实现期望的结构化输出。 所提出的方法通过有效结合底层多值黑箱输出函数的多个输出之间的相关性，提供了新的获取函数，以最小化期望的结构化输出与高斯过程模型预测之间的误差。 通过将其应用于两个合成形状搜索问题和真实数据，验证了所提出方法的有效性。 在真实数据实验中，我们解决了输入参数搜索问题，以在碳化硅（SiC）晶体生长建模中实现期望的晶体生长速率，这是一个材料信息学问题。