Preference-Based Multi-Objective Bayesian Optimization with Gradients

Abstract

We propose PUB-MOBO for personalized multi-objective Bayesian Optimization. PUB-MOBO combines utility-based MOBO with local multi-gradient descent to refine user-preferred solutions to be near-Pareto-optimal. Unlike traditional methods, PUB-MOBO does not require estimating the entire Pareto-front, making it more efficient. Experimental results on synthetic and real-world benchmarks show that PUB-MOBO consistently outperforms existing methods in terms of proximity to the Pareto-front and utility regret.

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Cite

Text

Ip et al. "Preference-Based Multi-Objective Bayesian Optimization with Gradients." NeurIPS 2024 Workshops: BDU, 2024.

Markdown

[Ip et al. "Preference-Based Multi-Objective Bayesian Optimization with Gradients." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/ip2024neuripsw-preferencebased/)

BibTeX

@inproceedings{ip2024neuripsw-preferencebased,
  title     = {{Preference-Based Multi-Objective Bayesian Optimization with Gradients}},
  author    = {Ip, Joshua Hang Sai and Chakrabarty, Ankush and Masui, Hideyuki and Mesbah, Ali and Romeres, Diego},
  booktitle = {NeurIPS 2024 Workshops: BDU},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/ip2024neuripsw-preferencebased/}
}