Moment-Based Domain Adaptation: Learning Bounds and Algorithms
This work addresses domain adaptation for machine learning applications where training and test distributions differ, but it appears incremental as it builds on existing frameworks with weaker assumptions.
The thesis tackled the problem of domain adaptation under weak similarity assumptions modeled by finitely many moments, contributing to its mathematical foundation by deriving learning bounds and algorithms.
This thesis contributes to the mathematical foundation of domain adaptation as emerging field in machine learning. In contrast to classical statistical learning, the framework of domain adaptation takes into account deviations between probability distributions in the training and application setting. Domain adaptation applies for a wider range of applications as future samples often follow a distribution that differs from the ones of the training samples. A decisive point is the generality of the assumptions about the similarity of the distributions. Therefore, in this thesis we study domain adaptation problems under as weak similarity assumptions as can be modelled by finitely many moments.