Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks

2026-04-14

AI Summary

Offline black-box optimization often struggles to effectively discover optimal designs—such as molecules or materials—due to scarce or low-quality data. To address this challenge, this work proposes OptBias, a novel framework that, for the first time, integrates meta-learning with synthetic tasks generated by Gaussian processes to explicitly model and transfer optimization preferences. The approach further adapts a surrogate model to the target small dataset through fine-tuning. OptBias is unified in its applicability to both continuous and discrete design spaces and substantially mitigates the poor ranking performance of surrogate models under limited data. Empirical results demonstrate that OptBias consistently outperforms state-of-the-art methods across multiple offline optimization benchmarks, particularly excelling in low-data regimes, and exhibits strong robustness and practical utility.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past experimental data. A key challenge in this setting is data scarcity: in many scientific applications, only small or poor-quality datasets are available, which severely limits the effectiveness of existing algorithms. Prior work has theoretically and empirically shown that performance of offline optimization algorithms depends on how well the surrogate model captures the optimization bias (i.e., ability to rank input designs correctly), which is challenging to accomplish with limited experimental data. This paper proposes Surrogate Learning with Optimization Bias via Synthetic Task Generation (OptBias), a meta-learning framework that directly tackles data scarcity. OptBias learns a reusable optimization bias by training on synthetic tasks generated from a Gaussian process, and then fine-tunes the surrogate model on the small data for the target task. Across diverse continuous and discrete offline optimization benchmarks, OptBias consistently outperforms state-of-the-art baselines in small data regimes. These results highlight OptBias as a robust and practical solution for offline optimization in realistic small data settings.

Problem

• offline black-box optimization
• data scarcity
• surrogate model
• optimization bias
• small datasets

Innovation

• meta-learning
• offline optimization
• data scarcity
• synthetic tasks
• surrogate modeling