# tidyHEBO: Improving the Robustness of Heteroskedastic Evolutionary Bayesian Optimization

arXiv: https://arxiv.org/abs/2607.10669 · HTML: https://mlsanity.com/paper/2607.10669/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Vanilla Bayesian Optimization Performs Great in High Dimensions](https://mlsanity.com/paper/2402.02229/) (2024)
- [HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation](https://mlsanity.com/paper/2012.03826/) (2020)
- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)
- [Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains](https://mlsanity.com/paper/2106.01309/) (2021)
- [Bayesian Optimization of Composite Functions](https://mlsanity.com/paper/1906.01537/) (2019)
- [Bayesian Optimization using Deep Gaussian Processes](https://mlsanity.com/paper/1905.03350/) (2019)
- [Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)](https://mlsanity.com/paper/2208.13771/) (2022)
- [Revisiting Bayesian Optimization in the light of the COCO benchmark](https://mlsanity.com/paper/2103.16649/) (2021)

## Abstract (arXiv)

Bayesian optimization is widely used for expensive black-box optimization problems. While methodological advances often focus on individual components, practical performance depends on how these components work together. We present tidyHEBO, a Bayesian optimization model. tidyHEBO jointly learns output warping and Gaussian-process parameters, evaluates acquisition functions on the original objective scale, and preserves promising novel candidates throughout evolutionary search. On the Olympus benchmark, tidyHEBO ranked first among the evaluated methods, achieving the best average ranks for typical optimization quality, worst-tail outcomes, and run-to-run variability. It also obtained results comparable to specialized optimizers on Needle-in-a-Haystack problems. On the Bayesmark benchmark, tidyHEBO also demonstrated strong results in hyperparameter tuning. Across these benchmarks, tidyHEBO combines strong optimization results with robustness across diverse black-box problems.