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机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多任务贝叶斯优化的陷阱与补救措施，发现其在简单情况下也会误估跨任务相关性，将失败归因于任务标准化和边际似然问题，提出三种补救措施，在简单实例上有效果，复杂实例中仍存在问题。
AI 中文摘要
贝叶斯优化通常会利用相关源任务的数据对目标实验进行热启动，多任务高斯过程是用于此任务的典型替代模型。我们在可控环境下重新审视了这一默认设置，发现即使在最简单的非平凡情况下，即仿射相关的源任务和目标任务中，它也会错误估计跨任务相关性，而有效的迁移学习方法在此应能成功。我们将失败归因于两个独立的结构机制。任务标准化（解决仿射切片模糊性的典型方法）会将有限样本对齐误差传播到恢复的相关性中。边际似然本身仅以高斯过程在非重叠设计中进一步稀释的样本速率识别相关性。我们从分析中提出了三种保守的补救措施：将每个任务的均值和尺度提升为模型参数，将任务协方差限制为非负相关性，以及将部分源设计和目标设计共定位。在合成多任务问题和基于替代模型的超参数调整迁移中，这些补救措施在简单实例上恢复了仅针对目标的基线，而在更难的实例以及大多数基于秩和潜在上下文的变体中，更广泛的失败仍然存在。
英文摘要
Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We revisit this default in a controlled setting and find that it misestimates the cross-task correlation even in the simplest non-trivial case, affinely related source and target tasks, where a working transfer learning method should obviously succeed. We trace the failure to two independent structural mechanisms. Per-task standardization, the textbook fix for the affine slice ambiguity, propagates a finite-sample alignment error into the recovered correlation. The marginal likelihood itself identifies the correlation only at a per-sample rate that a Gaussian process at non-overlapping designs further dilutes. We propose three conservative remedies that follow from the analysis: promoting per-task means and scales to model parameters, restricting the task covariance to non-negative correlations, and co-locating part of the source and target designs. Across synthetic multi-task problems and surrogate-based hyperparameter tuning transfer, these remedies recover the target-only baseline on the simple instances, while the broader failure persists on harder instances and across most rank-based and latent-context variants.
