# Efficient Bayesian Optimization using Multiscale Graph Correlation

arXiv: https://arxiv.org/abs/2103.09434 · HTML: https://mlsanity.com/paper/2103.09434/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [From Distance Correlation to Multiscale Graph Correlation](https://mlsanity.com/paper/1710.09768/) (2017)
- [Using Distance Correlation for Efficient Bayesian Optimization](https://mlsanity.com/paper/2102.08993/) (2021)
- [Discovering and Deciphering Relationships Across Disparate Data Modalities](https://mlsanity.com/paper/1609.05148/) (2016)

## Built on by (most cited first, 1 of 1)

- [One-parameter family of acquisition functions for efficient global optimization](https://mlsanity.com/paper/2104.12363/) (2021)

## Abstract (arXiv)

Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly. In this paper, we propose a new approach to Bayesian optimization called GP-MGC, which maximizes multiscale graph correlation with respect to the global maximum to determine the next query point. We present our evaluation of GP-MGC in applications involving both synthetic benchmark functions and real-world datasets and demonstrate that GP-MGC performs as well as or even better than state-of-the-art methods such as max-value entropy search and GP-UCB.