Time-Varying Propensity Score to Bridge the Gap between the Past and Present

arXiv:2210.01422v5 Announce Type: replace
Abstract: Real-world deployment of machine learning models is challenging because data evolves over time. While no model can work when data evolves in an arbitrary fashion, if there is some pattern to these changes, we might be able to design methods to address it. This paper addresses situations when data evolves gradually. We introduce a time-varying propensity score that can detect gradual shifts in the distribution of data which allows us to selectively sample past data …

abstract arxiv bridge cs.lg data deployment design fashion gap machine machine learning machine learning models pattern type while work world