Voronoi-grid sampling with a distance penalty produces Pareto fronts with higher hypervolume and better boundary coverage than existing Pareto front learning methods.
K-means V oronoi Jura 0.928 0.922 0.928 0.923 0.925 0.935 SARCOS 0.884 0.883 0.881 0.888 0.877 0.949 B.5
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Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Voronoi-grid sampling with a distance penalty produces Pareto fronts with higher hypervolume and better boundary coverage than existing Pareto front learning methods.