Transductive adaptation of black box predictions

Abstract

Access to data is critical to any machine learning component aimed at training an accurate predictive model. In reality, data is often a subject of technical and legal constraints. Data may contain sensitive topics and data owners are often reluctant to share them. Instead of access to data, they make available decision making procedures to enable predictions on new data. Under the black box classifier constraint, we build an effective domain adaptation technique which adapts classifier predictions in a transductive setting. We run experiments on text categorization datasets and show that significant gains can be achieved, especially in the unsupervised case where no labels are available in the target domain.