AI RESEARCH
SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
arXiv CS.LG
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ArXi:2601.22131v2 Announce Type: replace Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the budget of evaluations. In many applications, historical data from related optimization tasks is available and can be leveraged via meta-learning to accelerate optimization.