# Sequential Preference-Based Optimization

arXiv: https://arxiv.org/abs/1801.02788 · HTML: https://mlsanity.com/paper/1801.02788/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [Efficient Exploration in Binary and Preferential Bayesian Optimization](https://mlsanity.com/paper/2110.09361/) (2021)
- [Ranking over Regression for Bayesian Optimization and Molecule Selection](https://mlsanity.com/paper/2410.09290/) (2024)
- [Contextual Bayesian optimization with binary outputs](https://mlsanity.com/paper/2111.03447/) (2021)
- [Identifying Copeland Winners in Dueling Bandits with Indifferences](https://mlsanity.com/paper/2310.00750/) (2023)
- [Local Preferential Bayesian Optimization](https://mlsanity.com/paper/2606.02351/) (2026)

## Abstract (arXiv)

Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow for observations of equivalent preference from users.