Covariate shift adaptation and dataset shift decomposition in machine learning

• Ano de defesa: 2021
• Autor(a) principal: Polo, Felipe Maia
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Adaptação de dominio; Concept drift; Covariate shift; Dataset shift; Dataset shift decomposition; Decomposição do dataset shift; Dimensionalidade; Dimensionality; Domain adaptation; Effective sample size; Estatistica; Machine learning; Machine searning; Statistics
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/45/45133/tde-03022022-234955/

In supervised learning, we often have access to a limited sample, in size or quality (e.g., lack of labels), of the population/distribution of interest, for which we want to create predictive models. However, it is possible that we have less limited access to data sampled from another population, more or less similar to the one of interest. Training models using only data from the population of interest may be impossible or result in sub-optimal models, so it would be interesting to use data from the other population in order to get better results or make training possible. In these situations, as the distributions of interest and the one that we can sample with few restrictions are different, we say that there is dataset shift. In dataset shift situations, employing domain adaptation techniques when training supervised models is essential for theoretical guarantees of good results in the population of interest. The two kinds of dataset shift we will discuss about in this work are covariate shift and concept drift/shift. The main objectives of this work are: (i) to review the main concepts and methods related to covariate shift and covariate shift adaptation; (ii) propose contributions to the covariate shift adaptation literature, connecting concepts present in modern literature; (iii) propose the decomposition of the dataset shift into covariate shift and expected concept drift/shift as a new approach to better understand situations in which we deal with dataset shift.