Figure A.1: Among all hyperparameter combinations considered in our grid search, a combination of ( We used a compute time of approximately 5600 CPU-hours of 2.90GHz Intel Xeon Gold In this section, we outline our anomaly detection experimental setting in more detail. Given a dataset (e.g., CIFAR-10) with In contrast to the "one-vs-rest" setting, in LOO we define one class of the In both "one-vs-rest" and LOO AD settings, we evaluate model representations in the following way: We show the pairs of items that change the most in distance in Table B.1. "stethoscope", which are semantically unrelated but perhaps have some slight visual similarity, tend We show the results in Fig. B.1. Table B.1: Distances between pairs of individual items from THINGS, ranked by the relative change in cosine The top items move much closer together under naive alignment, while the bottom ones move much farther apart. Figure B.1: How does the global structure of the representations change after alignment?

Representation learning usually involves pretraining a network on a large, diverse dataset using one of a handful of different types of supervision.

A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.

Out-of-distribution (OOD) generalization issue is a well-known challenge within deep learning tasks. In dynamic graphs, the change of temporal environments is regarded as the main cause of data distribution shift. While numerous OOD studies focusing on environment factors have achieved remarkable performance, they still fail to systematically solve the two issue of environment inference and utilization. In this work, we propose a novel dynamic graph learning model named EpoD based on prompt learning and structural causal model to comprehensively enhance both environment inference and utilization. Inspired by the superior performance of prompt learning in understanding underlying semantic and causal associations, we first design a self-prompted learning mechanism to infer unseen environment factors. We then rethink the role of environment variable within spatio-temporal causal structure model, and introduce a novel causal pathway where dynamic sub-graphs serve as mediating variables. The extracted dynamic subgraph can effectively capture the data distribution shift by incorporating the inferred environment variables into the node-wise dependencies.