SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization

• URL: http://arxiv.org/abs/2601.22131v1
• Date: Thu, 29 Jan 2026 18:51:58 GMT
• Title: SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
• Authors: Leonard Papenmeier, Petru Tighineanu,
• Abstract summary: We propose a scalable and modular meta-learning model based on a multi-output Gaussian process that explicitly learns correlations between objectives.<n> SMOG supports hierarchical, parallel training: meta-task Gaussian processes are fit once and then cached, achieving linear scaling with the number of meta-tasks.
• Score: 2.318371621318972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-objective optimization aims to solve problems with competing objectives, often with only black-box access to a problem and a limited budget of measurements. In many applications, historical data from related optimization tasks is available, creating an opportunity for meta-learning to accelerate the optimization. Bayesian optimization, as a promising technique for black-box optimization, has been extended to meta-learning and multi-objective optimization independently, but methods that simultaneously address both settings - meta-learned priors for multi-objective Bayesian optimization - remain largely unexplored. We propose SMOG, a scalable and modular meta-learning model based on a multi-output Gaussian process that explicitly learns correlations between objectives. SMOG builds a structured joint Gaussian process prior across meta- and target tasks and, after conditioning on metadata, yields a closed-form target-task prior augmented by a flexible residual multi-output kernel. This construction propagates metadata uncertainty into the target surrogate in a principled way. SMOG supports hierarchical, parallel training: meta-task Gaussian processes are fit once and then cached, achieving linear scaling with the number of meta-tasks. The resulting surrogate integrates seamlessly with standard multi-objective Bayesian optimization acquisition functions.

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