We investigate modifications to Bayesian Optimization for a
resource-constrained setting of sequential experimental design where changes to
certain design variables of the search space incur a switching cost. This
models the scenario where there is a trade-off between evaluating more while
maintaining the same setup, or switching and restricting the number of possible
evaluations due to the incurred cost. We adapt two process-constrained batch
algorithms to this sequential problem formulation, and propose two new methods:
one cost-aware and one cost-ignorant. We validate and compare the algorithms
using a set of 7 scalable test functions in different dimensionalities and
switching-cost settings for 30 total configurations. Our proposed cost-aware
hyperparameter-free algorithm yields comparable results to tuned
process-constrained algorithms in all settings we considered, suggesting some
degree of robustness to varying landscape features and cost trade-offs. This
method starts to outperform the other algorithms with increasing
switching-cost. Our work broadens out from other recent Bayesian Optimization
studies in resource-constrained settings that consider a batch setting only.
While the contributions of this work are relevant to the general class of
resource-constrained problems, they are particularly relevant to problems where
adaptability to varying resource availability is of high importance

在资源受限的顺序实验设计中，我们研究了贝叶斯优化的改进，其中搜索空间的某些设计变量的改变会产生切换成本。我们将两种过程受限的批处理算法调整为这个顺序问题的公式，并提出了两种新方法：一种考虑成本，一种忽略成本。通过使用一组 7 个具有不同维度和切换成本设置的可扩展测试函数以及 30 个总配置的验证和比较算法，我们的无超参数成本感知算法在我们考虑的所有设置中都产生了可比的结果，表明对于不同的景观特征和成本权衡具备一定的鲁棒性。随着切换成本的增加，这种方法开始在性能上超越其他算法。我们的工作与其他最近在资源受限设置中仅考虑批处理设置的贝叶斯优化研究有所不同。尽管这项工作的贡献与一般的资源受限问题相关，但在适应不同资源可用性的问题中尤为重要。

一种具有切换成本的自适应贝叶斯优化方法

An adaptive approach to Bayesian Optimization with switching costs

Finite-horizon sequential experimental design (SED) arises naturally in many
contexts, including hyperparameter tuning in machine learning among more
traditional settings. Computing the optimal policy for such problems requires
solving Bellman equations, which are generally intractable. Most existing work
resorts to severely myopic approximations by limiting the decision horizon to
only a single time-step, which can underweight exploration in favor of
exploitation. We present BINOCULARS: Batch-Informed NOnmyopic Choices, Using
Long-horizons for Adaptive, Rapid SED, a general framework for deriving
efficient, nonmyopic approximations to the optimal experimental policy. Our key
idea is simple and surprisingly effective: we first compute a one-step optimal
batch of experiments, then select a single point from this batch to evaluate.
We realize BINOCULARS for Bayesian optimization and Bayesian quadrature -- two
notable SED problems with radically different objectives -- and demonstrate
that BINOCULARS significantly outperforms myopic alternatives in real-world
scenarios.

该研究提出了一个基于贝叶斯优化的序列实验设计的新框架 ——BINOCULARS，它可以更有效，更准确地计算实验的最佳方案。

高效非近视序贯实验设计的双筒望远镜

BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design

BayesOpt is a library with state-of-the-art Bayesian optimization methods to
solve nonlinear optimization, stochastic bandits or sequential experimental
design problems. Bayesian optimization is sample efficient by building a
posterior distribution to capture the evidence and prior knowledge for the
target function. Built in standard C++, the library is extremely efficient
while being portable and flexible. It includes a common interface for C, C++,
Python, Matlab and Octave.

BayesOpt 是一个用于解决非线性优化、随机万字或序列实验设计问题的先进贝叶斯优化方法库。该库十分高效，具备通用的 C、C++、Python、Matlab 和 Octave 接口。