Consider binary classification where test examples are not from the training distribution.

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels.

We thank all the reviewers for their time and constructive comments. In practice, the most cost-effective K might be determined through cross-validation. We will improve the description to make it clear. The existing methods (e.g., Tong & Du [1]) require that the diffusion model is known to us, while our Therefore, the method in Tong & Du [1] is not applicable to our setting, and it is not used as a competitor.

We propose SemiFL to address the problem of combining communication-efficient FL such as FedAvg with Semi-Supervised Learning (SSL).

This is essentially by definition--intervention on Z doesn't change the potential outcomes, so it doesn't change the value of f (X). If f is a counterfactually invariant predictor: 1. Let L be either square error or cross entropy loss. Suppose that the target distribution Q is causally compatible with the training distribution P . Suppose that any of the following conditions hold: 1. the data obeys the anti-causal graph 2. the data obeys the causal-direction graph, there is no confounding (but possibly selection), and the association is purely spurious, Y X | X We begin with the anti-causal case.