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Poster
Hyperparameter Learning via Distributional Transfer
Ho Chung Law · Peilin Zhao · Leung Sing Chan · Junzhou Huang · Dino Sejdinovic

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #29
in Algorithms -- AutoML »

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 results in a joint Gaussian process model on hyperparameters and data representations. 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.

Keywords: Gaussian Processes · AutoML · Algorithms · Algorithms -> Kernel Methods; Algorithms -> Multitask and Transfer Learning; Probabilistic Methods
[ Paper]  [ Poster] 

Author Information

Ho Chung Law (University of Oxford)
Peilin Zhao (Tencent AI Lab)
Leung Sing Chan (University of Oxford)
Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)
Dino Sejdinovic (University of Oxford)

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