用合成任务训练优化偏差，小数据下也能高效找最优设计。
Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
- 通过高斯过程生成合成任务，学习可复用的优化偏好
- 在小数据集上显著优于现有最优算法
- 适合实验数据少的分子、材料等科学优化场景
我们研究离线黑箱优化问题，目标是从历史实验数据中发现最优设计（如分子或材料）。该任务的核心挑战是数据稀缺：许多科学应用中仅有小规模或低质量数据，严重限制了现有算法性能。已有研究证明，离线优化算法的表现取决于代理模型对优化偏好的捕捉能力（即正确排序输入设计的能力），而小样本下难以实现。本文提出基于合成任务生成的元学习框架OptBias，直接应对数据稀缺问题。OptBias通过在高斯过程生成的合成任务上训练，学习可复用的优化偏好，随后在小样本数据上微调代理模型以适应目标任务。在多种连续与离散的离线优化基准测试中，OptBias在小数据场景下始终优于现有最先进方法。结果表明，OptBias是真实小数据环境下离线优化的一种鲁棒且实用的解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。