# Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization

arXiv: https://arxiv.org/abs/2012.10335 · HTML: https://mlsanity.com/paper/2012.10335/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [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)
- [E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model](https://mlsanity.com/paper/2404.11581/) (2024)
- [Multi-objective Optimization by Learning Space Partitions](https://mlsanity.com/paper/2110.03173/) (2021)
- [Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation](https://mlsanity.com/paper/2307.13371/) (2023)
- [Learning Space Partitions for Path Planning](https://mlsanity.com/paper/2106.10544/) (2021)
- [ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution](https://mlsanity.com/paper/2205.09548/) (2022)
- [MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems](https://mlsanity.com/paper/2510.01523/) (2025)

## Abstract (arXiv)

Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named textttSPBOpt. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.