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.

本篇论文提出了一种新的图形模型预测方法，使用潜在偏移自适应性，利用最近提出的概率无监督学习框架来从图像中识别因果潜在结构，以适应和预测已知的源环境和未知的目标环境，结果显示本方法的高维图像数据集具有良好的预测性能。