Reviews: Transfer Learning via Minimizing the Performance Gap Between Domains

After rebuttal I thank the authors for their reply, they have managed to clarify some of my concerns and overall I vote for acceptance of the paper. This paper introduces a boosting method for transfer learning with instance re-weighting in the setting where labeled data are available in both training and target tasks. Theorem 1 provides a bound for the population error on the target task, and motivates four instance re-weighting principles''. A practical procedure is introduced, which achieves competitive results on two standard datasets for transfer learning. Novelty: To my knowledge, the theoretical analysis carried out by the authors in the context of fully labeled data is novel.