Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training distribution. Yet, learning theory of distribution shift bounds performance on the target distribution as a function of the discrepancy between the source and target, rarely guaranteeing high target accuracy. Motivated by this gap, this work takes a closer look at the theory of distribution shift for a classifier from a source to a target distribution. Instead of relying on the discrepancy, we adopt an Invariant-Risk-Minimization (IRM)-like assumption connecting the distributions, and characterize conditions under which data from a source distribution is sufficient for accurate classification of the target. When these conditions are not met, we show when only unlabeled data from the target is sufficient, and when labeled target data is needed. In all cases, we provide rigorous theoretical guarantees in the large sample regime.

在分布转移理论中，通过采用不变风险最小化（IRM）类似的假设连接分布，研究源分布到目标分布的分类器，揭示了源分布数据足够准确分类目标的条件，并讨论了在这些条件不满足时，只需目标的无标签数据或标记目标数据的情况，并提供了严格的理论保证。

超越差异：对分布偏移理论的深入研究