A Kernel for Hierarchical Parameter Spaces

31 citations recorded by the source corpus.

We define a family of kernels for mixed continuous/discrete hierarchical parameter spaces and show that they are positive definite.

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Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces (2014, background): “In this section, we construct a kernel between points in a space that may have dimensions which are irrelevant under known conditions (further details are available in [14]).”
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA (2017, background): “Conditional dependencies can also be accommodated in the Gaussian process framework (Hutter and Osborne, 2013; Swersky et al., 2013), but currently, tree-based methods achieve better performance. budget available for the entire learning process.”
Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters (2013, background): “These conditional parameters are, for example, common in deep architectures, or in frameworks including many alternative algorithms, and some hyperparameter optimizers exploit knowledge about these conditionalities in their models to improve their performance [9, 8, 11, 12].”