Poster
in
Workshop: Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design

Preference-based Multi-Objective Bayesian Optimization with Gradients

Joshua Hang Sai Ip ⋅ Ankush Chakrabarty ⋅ Ali Mesbah ⋅ Diego Romeres

Keywords: Human-in-the-loop Machine Learning Bayesian Learning machine learning

Project Page [ OpenReview]

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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