We introduce a new approach to prediction in graphical models with latent-shift adaptation, i.e., where source and target environments differ in the distribution of an unobserved confounding latent variable. Previous work has shown that as long as "concept" and "proxy" variables with appropriate dependence are observed in the source environment, the latent-associated distributional changes can be identified, and target predictions adapted accurately. However, practical estimation methods do not scale well when the observations are complex and high-dimensional, even if the confounding latent is categorical. Here we build upon a recently proposed probabilistic unsupervised learning framework, the recognition-parametrised model (RPM), to recover low-dimensional, discrete latents from image observations. Applied to the problem of latent shifts, our novel form of RPM identifies causal latent structure in the source environment, and adapts properly to predict in the target. We demonstrate results in settings where predictor and proxy are high-dimensional images, a context to which previous methods fail to scale.

翻译：我们提出了一种在潜变量偏移适应场景下进行图模型预测的新方法，即源环境与目标环境中未观测到的混杂潜变量分布存在差异的情形。先前研究已表明，只要源环境中观测到具有恰当依赖关系的"概念"变量与"代理"变量，即可识别潜变量相关的分布变化，并准确调整目标预测结果。然而，当观测数据复杂且高维时，即使混杂潜变量为类别型变量，现有的实用估计方法也难以有效扩展。本文基于近期提出的概率无监督学习框架——识别参数化模型，从图像观测中恢复低维离散潜变量。针对潜变量偏移问题，我们提出的新型RPM方法能识别源环境中的因果潜结构，并自适应调整以实现目标环境中的准确预测。我们在预测变量与代理变量均为高维图像的场景中验证了该方法的效果——此类场景是先前方法难以扩展的适用范畴。