Continuous Invariance Learning

arXiv:2310.05348v2 Announce Type: replace
Abstract: Invariance learning methods aim to learn invariant features in the hope that they generalize under distributional shifts. Although many tasks are naturally characterized by continuous domains, current invariance learning techniques generally assume categorically indexed domains. For example, auto-scaling in cloud computing often needs a CPU utilization prediction model that generalizes across different times (e.g., time of a day and date of a year), where `time' is a continuous domain index. In this paper, we start …

abstract aim arxiv auto cloud cloud computing computing continuous cpu cs.ai cs.lg current domains example features learn prediction scaling tasks type