论文标题
黑盒优化的生成预处理
Generative Pretraining for Black-Box Optimization
论文作者
论文摘要
科学和工程中的许多问题涉及在高维空间上优化昂贵的黑盒功能。对于此类黑盒优化（BBO）问题，我们通常会为在线功能评估的预算较小，但通常也可以访问固定的离线数据集进行预处理。先前的方法试图利用离线数据来近似函数或逆向，但距离数据分布还不够精确。我们提出了Bonet，这是一种使用离线数据集预处理新颖的黑盒优化器的生成框架。在BONET中，我们对从离线数据集衍生的固定长度轨迹训练自回旋模型。我们设计了一种简单的启发式术语，即从低保真度到高保真样本中，使用简单的启发式术语来综合轨迹。从经验上讲，我们使用因果掩盖的变压器实例化锁骨，并在设计基础上进行评估，在设计基础上，我们平均排名最优于最优于最先进的基线。
Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often have access to a fixed, offline dataset for pretraining. Prior approaches seek to utilize the offline data to approximate the function or its inverse but are not sufficiently accurate far from the data distribution. We propose BONET, a generative framework for pretraining a novel black-box optimizer using offline datasets. In BONET, we train an autoregressive model on fixed-length trajectories derived from an offline dataset. We design a sampling strategy to synthesize trajectories from offline data using a simple heuristic of rolling out monotonic transitions from low-fidelity to high-fidelity samples. Empirically, we instantiate BONET using a causally masked Transformer and evaluate it on Design-Bench, where we rank the best on average, outperforming state-of-the-art baselines.
