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• W2770570483 abstract "This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objective function f for boosting the BO performance and still preserve scalability in the number of input dimensions without requiring prior knowledge or the existence of a low (effective) dimension of the input space. To realize this, we propose a sparse yet rich factor graph representation of f to be exploited for designing an acquisition function that can be similarly represented by a sparse factor graph and hence be efficiently optimized in a decentralized manner using distributed message passing. Despite richly characterizing the interdependent effects of the input components on the output of f with a factor graph, DEC-HBO can still guarantee no-regret performance asymptotically. Empirical evaluation on synthetic and real-world experiments (e.g., sparse Gaussian process model with 1811 hyperparameters) shows that DEC-HBO outperforms the state-of-the-art HBO algorithms." @default.
• W2770570483 created "2017-12-04" @default.
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• W2770570483 date "2018-04-29" @default.
• W2770570483 modified "2023-10-16" @default.
• W2770570483 title "Decentralized High-Dimensional Bayesian Optimization With Factor Graphs" @default.
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• W2770570483 doi "https://doi.org/10.1609/aaai.v32i1.11788" @default.
• W2770570483 hasPublicationYear "2018" @default.
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