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 ]
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.