# Using Distance Correlation for Efficient Bayesian Optimization

arXiv: https://arxiv.org/abs/2102.08993 · HTML: https://mlsanity.com/paper/2102.08993/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 71 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 26 of 26)

- [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)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [Bayesian Optimization with Gradients](https://mlsanity.com/paper/1703.04389/) (2017)
- [Multi-objective Bayesian Optimization using Pareto-frontier Entropy](https://mlsanity.com/paper/1906.00127/) (2019)
- [Learning When and Where to Zoom with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00425/) (2020)
- [Scaling Gaussian Process Regression with Derivatives](https://mlsanity.com/paper/1810.12283/) (2018)
- [MUMBO: MUlti-task Max-value Bayesian Optimization](https://mlsanity.com/paper/2006.12093/) (2020)
- [Multi-task Learning for Aggregated Data using Gaussian Processes](https://mlsanity.com/paper/1906.09412/) (2019)
- [Variational Learning on Aggregate Outputs with Gaussian Processes](https://mlsanity.com/paper/1805.08463/) (2018)
- [Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs](https://mlsanity.com/paper/1907.08350/) (2019)
- [Gaussian Process Optimization with Mutual Information](https://mlsanity.com/paper/1311.4825/) (2013)
- [Multi-resolution Multi-task Gaussian Processes](https://mlsanity.com/paper/1906.08344/) (2019)
- [Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature](https://mlsanity.com/paper/1704.00060/) (2017)
- [Sequentially optimized projections in X-ray imaging](https://mlsanity.com/paper/2006.12579/) (2020)
- [Probabilistic approach to limited-data computed tomography reconstruction](https://mlsanity.com/paper/1809.03779/) (2018)
- [Gaussian Process Regression for Binned Data](https://mlsanity.com/paper/1809.02010/) (2018)
- [Evaluating the squared-exponential covariance function in Gaussian processes with integral observations](https://mlsanity.com/paper/1812.07319/) (2018)
- [Accelerating small-angle scattering experiments with simulation-based machine learning](https://mlsanity.com/paper/1908.09102/) (2019)
- [Efficient Poverty Mapping using Deep Reinforcement Learning](https://mlsanity.com/paper/2006.04224/) (2020)
- [Deep kernel learning for integral measurements](https://mlsanity.com/paper/1909.01844/) (2019)

## Built on by (most cited first, 3 of 3)

- [Leveraging Trust for Joint Multi-Objective and Multi-Fidelity Optimization](https://mlsanity.com/paper/2112.13901/) (2021)
- [Efficient Bayesian Optimization using Multiscale Graph Correlation](https://mlsanity.com/paper/2103.09434/) (2021)
- [One-parameter family of acquisition functions for efficient global optimization](https://mlsanity.com/paper/2104.12363/) (2021)

## Abstract (arXiv)

The need to collect data via expensive measurements of black-box functions is prevalent across science, engineering and medicine. As an example, hyperparameter tuning of a large AI model is critical to its predictive performance but is generally time-consuming and unwieldy. Bayesian optimization (BO) is a collection of methods that aim to address this issue by means of Bayesian statistical inference. In this work, we put forward a BO scheme named BDC, which integrates BO with a statistical measure of association of two random variables called Distance Correlation. BDC balances exploration and exploitation automatically, and requires no manual hyperparameter tuning. We evaluate BDC on a range of benchmark tests and observe that it performs on per with popular BO methods such as the expected improvement and max-value entropy search. We also apply BDC to optimization of sequential integral observations of an unknown terrain and confirm its utility.