We study Bayesian optimization (BO) in high-dimensional and non-stationary
scenarios. Existing algorithms for such scenarios typically require extensive
hyperparameter tuning, which limits their practical effectiveness. We propose a
framework, called BALLET, which adaptively filters for a high-confidence region
of interest (ROI) as a superlevel-set of a nonparametric probabilistic model
such as a Gaussian process (GP). Our approach is easy to tune, and is able to
focus on local region of the optimization space that can be tackled by existing
BO methods. The key idea is to use two probabilistic models: a coarse GP to
identify the ROI, and a localized GP for optimization within the ROI. We show
theoretically that BALLET can efficiently shrink the search space, and can
exhibit a tighter regret bound than standard BO without ROI filtering. We
demonstrate empirically the effectiveness of BALLET on both synthetic and
real-world optimization tasks.

我们研究高维和非稳态情景下的贝叶斯优化。我们提出了一个名为 BALLET 的框架，通过自适应过滤高置信度感兴趣区域（ROI）来解决现有算法在这些情景中通常需要大量超参数调整的问题。我们的方法易于调整，并能够聚焦于可以应用现有贝叶斯优化方法处理的优化空间的局部区域。关键思想是使用两个概率模型：一个粗糙的高斯过程（GP）用于识别 ROI，一个局部化的 GP 用于 ROI 内的优化。我们理论上证明了 BALLET 可以有效缩小搜索空间，并且能够比没有 ROI 过滤的标准贝叶斯优化展现更紧的遗憾界限。我们通过合成和实际优化任务的实证研究证明了 BALLET 的有效性。

自适应水平集估计的贝叶斯优化感兴趣区域学习

Learning Regions of Interest for Bayesian Optimization with Adaptive  Level-Set Estimation

Applications that generate huge amounts of data in the form of fast streams
are becoming increasingly prevalent, being therefore necessary to learn in an
online manner. These conditions usually impose memory and processing time
restrictions, and they often turn into evolving environments where a change may
affect the input data distribution. Such a change causes that predictive models
trained over these stream data become obsolete and do not adapt suitably to new
distributions. Specially in these non-stationary scenarios, there is a pressing
need for new algorithms that adapt to these changes as fast as possible, while
maintaining good performance scores. Unfortunately, most off-the-shelf
classification models need to be retrained if they are used in changing
environments, and fail to scale properly. Spiking Neural Networks have revealed
themselves as one of the most successful approaches to model the behavior and
learning potential of the brain, and exploit them to undertake practical online
learning tasks. Besides, some specific flavors of Spiking Neural Networks can
overcome the necessity of retraining after a drift occurs. This work intends to
merge both fields by serving as a comprehensive overview, motivating further
developments that embrace Spiking Neural Networks for online learning
scenarios, and being a friendly entry point for non-experts.

本研究综述了脉冲神经网络在在线学习场景中的应用，特别是在非平稳环境和数据漂移发生时的优势。