突破时间变化优化的采样频率限制，首次给出可变采样下的性能保证。
Time-Varying Bayesian Optimization Without a Metronome
- 放宽固定采样率假设，建立可变采样频率下的理论上限。
- 提出数据规模与过期数据管理策略，提升长期优化效率。
- 实验证明新方法在合成与真实任务中优于现有最优算法。
时间变化贝叶斯优化（TVBO）是优化随时间变化、昂贵且噪声干扰的黑箱函数 $f$ 的主流框架。然而，多数 TVBO 算法的渐近性能保证依赖于观测以恒定频率获取的假设。由于高斯过程（GP）推断复杂度随数据集大小的立方增长，这一假设在长期运行中不现实。本文首次推导出明确考虑观测采样频率变化的上界后悔值。基于该分析，我们提出了关于数据集大小和过期数据处理策略的实际建议。实验表明，遵循这些建议的算法（BOLT）在合成与真实世界问题上均优于当前最优的 TVBO 方法。
原文摘要 · Abstract (English)
Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying, expensive, noisy black-box function $f$. However, most of the asymptotic guarantees offered by TVBO algorithms rely on the assumption that observations are acquired at a constant frequency. As the GP inference complexity scales with the cube of its dataset size, this assumption is unrealistic in the long run. In this paper, we relax this assumption and derive the first upper regret bound that explicitly accounts for changes in the observations sampling frequency. Based on this analysis, we formulate practical recommendations about dataset sizes and stale data policies of TVBO algorithms. We illustrate how an algorithm (BOLT) that follows these recommendations performs better than the state-of-the-art of TVBO through experiments on synthetic and real-world problems.
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。