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SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
SMOG proposes a scalable multi-output Gaussian process meta-learning model that learns objective correlations to accelerate multi-objective Bayesian optimization with linear meta-task scaling.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5