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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO.
Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
To address these limitations, we design a Geometry-Aware Pareto set Learning algorithm named GAPL, which provides a novel geometric perspective for neural MOCO via a Pareto attention model based on hypervolume expectation maximization.
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