Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Additive Tree-Structured Conditional Parameter Spaces in Bayesian
  Optimization: A Novel Covariance Function and a Fast Implementation   Bayesian optimization (BO) is a sample-efficient global optimization
algorithm for black-box functions which are expensive to evaluate. Existing
literature on model based optimization in conditional parameter spaces are
usually built on trees. In this work, we generalize the additive assumption to
tree-structured functions and propose an additive tree-structured covariance
function, showing improved sample-efficiency, wider applicability and greater
flexibility. Furthermore, by incorporating the structure information of
parameter spaces and the additive assumption in the BO loop, we develop a
parallel algorithm to optimize the acquisition function and this optimization
can be performed in a low dimensional space. We demonstrate our method on an
optimization benchmark function, on a neural network compression problem, on
pruning pre-trained VGG16 and ResNet50 models as well as on searching
activation functions of ResNet20. Experimental results show our approach
significantly outperforms the current state of the art for conditional
parameter optimization including SMAC, TPE and Jenatton et al. (2017).

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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
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The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: chem-ph