揭穿泛化错误或：我如何学会停止担心并爱上我的训练集
Debunking Generalization Error or: How I Learned to Stop Worrying and Love My Training Set
摘要：
我们的目标是使用监督机器学习方法，从观测到的光谱中确定遥远星系的一些物理性质（例如，恒星质量，恒星形成历史或化学富集历史）。我们知道，不同的天体物理过程会在光谱的不同区域留下具有特征的印记。不幸的是，识别这个问题的训练集非常困难，因为标签并不容易获得-我们无法知道星系如何形成的真实历史。解决这个问题的一种可能方法是在最先进的宇宙学模拟上训练机器学习模型。然而，当在模拟上训练算法时，尚不清楚它们一旦应用于真实数据时的表现如何。在本文中，我们试图将泛化误差建模为源域和应用域之间距离的适当度量的函数。我们的目标是获得一个可靠的估计，即在模拟上训练的模型如何在数据上表现。
英文摘要：
We aim to determine some physical properties of distant galaxies (for example, stellar mass, star formation history, or chemical enrichment history) from their observed spectra, using supervised machine learning methods. We know that different astrophysical processes leave their imprint in various regions of the spectra with characteristic signatures. Unfortunately, identifying a training set for this problem is very hard, because labels are not readily available - we have no way of knowing the true history of how galaxies have formed. One possible approach to this problem is to train machine learning models on state-of-the-art cosmological simulations. However, when algorithms are trained on the simulations, it is unclear how well they will perform once applied to real data. In this paper, we attempt to model the generalization error as a function of an appropriate measure of distance between the source domain and the application domain. Our goal is to obtain a reliable estimate of how a model trained on simulations might behave on data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
