# Random Postprocessing for Combinatorial Bayesian Optimization

arXiv: https://arxiv.org/abs/2309.02842 · HTML: https://mlsanity.com/paper/2309.02842/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Mercer Features for Efficient Combinatorial Bayesian Optimization](https://mlsanity.com/paper/2012.07762/) (2020)
- [Bayesian Optimization over Permutation Spaces](https://mlsanity.com/paper/2112.01049/) (2021)
- [Application of QUBO solver using black-box optimization to structural design for resonance avoidance](https://mlsanity.com/paper/2204.04906/) (2022)
- [Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming](https://mlsanity.com/paper/2204.10579/) (2022)
- [Fourier Representations for Black-Box Optimization over Categorical Variables](https://mlsanity.com/paper/2202.03712/) (2022)

## Built on by (most cited first, 2 of 2)

- [Subsampling Factorization Machine Annealing](https://mlsanity.com/paper/2508.08778/) (2025)
- [Filtering out mislabeled training instances using black-box optimization and quantum annealing](https://mlsanity.com/paper/2501.06916/) (2025)

## Abstract (arXiv)

Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objective function in interest, resulting in many steps to find the global optimum. Here, we numerically study the effect of a postprocessing method on Bayesian optimization that strictly prohibits duplicated samples in the dataset. We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum, especially when the acquisition function is of maximum a posterior estimation. Our results provide a simple but general strategy to solve the slow convergence of Bayesian optimization for high-dimensional problems.