Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift

arXiv:2306.16406v3 Announce Type: replace-cross
Abstract: Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such \emph{dataset shift} conditions are known as \emph{domain adaptation} or \emph{transfer learning}. Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the …

abstract arxiv challenge data dataset domain face forms general machine machine learning math.st population risk robust shift statistical stat.me stat.ml stat.th type