Sequential Preference-Based Optimization
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2021Efficient Exploration in Binary and Preferential Bayesian Optimizationcited by 5
- 2024Ranking over Regression for Bayesian Optimization and Molecule Selectioncited by 4
- 2021Contextual Bayesian optimization with binary outputscited by 1
- 2023Identifying Copeland Winners in Dueling Bandits with Indifferencescited by 0
- 2026Local Preferential Bayesian Optimizationcited by 0
- 2026Local Preferential Bayesian Optimization1 Jun 2026
- 2024Ranking over Regression for Bayesian Optimization and Molecule Selection11 Oct 2024
- 2023Identifying Copeland Winners in Dueling Bandits with Indifferences1 Oct 2023
- 2021Contextual Bayesian optimization with binary outputs5 Nov 2021
- 2021Efficient Exploration in Binary and Preferential Bayesian Optimization18 Oct 2021