Review for NeurIPS paper: Learning to Adapt to Evolving Domains

Weaknesses: - Problem setting: The motivation behind the combination of domain adaptation and online learning can be further elaborated. Online learning is mainly used when it is computationally infeasible to train over the entire dataset. In domain adaptation, only very few examples are used for the adaptation process. Therefore it is usually feasible to simply store all the data and train in an off-line manner. As shown in Table 1, the performance of the proposed framework is weak, making it impractical to choose the online learning setting.