Reviews: Hyperparameter Learning via Distributional Transfer

This paper proposed a novel method for transfer learning in Bayesian hyperparameter optimization based on the theory that the distributions of previously observed datasets contain significant information that should not be ignored during hyperparameter optimization on a new dataset. They propose solutions to compare different datasets through distribution estimation and then combine this information with the classical Bayesian hyperparameter optimization setup. Experiments show that the method outperforms selected baselines. Originality: the method is novel, although it mostly bridges ideas from various fields. Quality: I would like to congratulate the authors on a very well written paper.