论文标题
通过概念和代理适应潜在的亚组转移
Adapting to Latent Subgroup Shifts via Concepts and Proxies
论文作者
论文摘要
当源域与目标域不同时，我们解决了无监督域的适应性问题，因为潜在亚组的分布发生了变化。当该亚组混淆所有观察到的数据时，既不适用协变量偏移也不适用标签偏移假设。我们表明，最佳目标预测指标只能在源域中可用的概念和代理变量的帮助以及来自目标的未标记数据来非参数识别。识别结果是建设性的，立即提出了一种用于估计目标中最佳预测因子的算法。对于连续观察，当该算法变得不切实际时，我们提出了一个特定于手头数据生成过程的潜在变量模型。我们展示了随着移位的大小而变化的方法，并验证其表现优于协变量和标签移位调整。
We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor label shift assumptions apply. We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target. The identification results are constructive, immediately suggesting an algorithm for estimating the optimal predictor in the target. For continuous observations, when this algorithm becomes impractical, we propose a latent variable model specific to the data generation process at hand. We show how the approach degrades as the size of the shift changes, and verify that it outperforms both covariate and label shift adjustment.
