Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains

arXiv:2402.14145v1 Announce Type: cross
Abstract: Distribution shifts are ubiquitous in real-world machine learning applications, posing a challenge to the generalization of models trained on one data distribution to another. We focus on scenarios where data distributions vary across multiple segments of the entire population and only make local assumptions about the differences between training and test (deployment) distributions within each segment. We propose a two-stage multiply robust estimation method to improve model performance on each individual segment for tabular data …

abstract applications arxiv assumptions challenge cs.lg data distribution domains focus machine machine learning machine learning applications multiple population robust stat.me stat.ml type world