Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks.

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.

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective.

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective. Papers published at the Neural Information Processing Systems Conference.