An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
This work addresses domain adaptation for machine learning practitioners by offering incremental theoretical improvements to existing bounds.
The paper tackles the problem of domain adaptation by proposing a tighter and more interpretable generalization bound based on PAC-Bayesian theory, improving upon previous work by Germain et al., and provides a new analysis of a constant term to aid algorithm development.
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germain et al. in two ways. We first give another generalization bound tighter and easier to interpret. Moreover, we provide a new analysis of the constant term appearing in the bound that can be of high interest for developing new algorithmic solutions.