提出BEEBO方法，实现高效批处理贝叶斯优化
Batched Energy-Entropy acquisition for Bayesian Optimization
- 基于统计物理思想设计能量-熵采集函数，原生支持批量评估
- 可精准控制探索与利用平衡，在异方差问题上表现良好
- 适合需要并行采样的高成本优化场景，如超参调优
贝叶斯优化（BO）是一种高效的全局优化黑箱函数的机器学习框架。其优化过程由采集函数指导，用于每轮选择待评估点。在批处理贝叶斯优化中，当并行获取多个点时，常用采集函数往往维度高且不可计算，常需依赖采样方法替代。本文提出一种受统计物理启发的采集函数——批处理能量-熵采集（BEEBO），适用于高斯过程模型，可原生处理批量点。BEEBO能精细调控探索与利用的权衡，并推广至异方差黑箱问题。我们在多种任务上验证了BEEBO的有效性，性能与现有方法相当甚至更优。
原文摘要 · Abstract (English)
Bayesian optimization (BO) is an attractive machine learning framework for performing sample-efficient global optimization of black-box functions. The optimization process is guided by an acquisition function that selects points to acquire in each round of BO. In batched BO, when multiple points are acquired in parallel, commonly used acquisition functions are often high-dimensional and intractable, leading to the use of sampling-based alternatives. We propose a statistical physics inspired acquisition function for BO with Gaussian processes that can natively handle batches. Batched Energy-Entropy acquisition for BO (BEEBO) enables tight control of the explore-exploit trade-off of the optimization process and generalizes to heteroskedastic black-box problems. We demonstrate the applicability of BEEBO on a range of problems, showing competitive performance to existing methods.
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