# Decentralized High-Dimensional Bayesian Optimization with Factor Graphs

arXiv: https://arxiv.org/abs/1711.07033 · HTML: https://mlsanity.com/paper/1711.07033/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2017.

## Built on by (most cited first, 23 of 23)

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- [On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization](https://mlsanity.com/paper/2510.11792/) (2025)
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## Abstract (arXiv)

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.