2022/06/06 by Dirk Tasche, Tasche, Dirk · 2 citations
Computer Science · Mathematics · #62B05 #Advanced Statistical Methods and Models #Anomaly Detection Techniques and Applications #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Mathematics #G.3 #I.5.1 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2206.02449
openalex publication_date 2022/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We show that in the context of classification the property of source and target distributions to be related by covariate shift may be lost if the information content captured in the covariates is reduced, for instance by dropping components or mapping into a lower-dimensional or finite space. As a consequence, under covariate shift simple approaches to class prior estimation in the style of classify and count with or without adjustment are infeasible. We prove that transformations of the covariates that preserve the covariate shift property are necessarily sufficient in the statistical sense for the full set of covariates. A probing algorithm as alternative approach to class prior estimation under covariate shift is proposed.