We introduce an efficient and robust auto-tuning framework for hyperparameter
selection in dimension reduction (DR) algorithms, focusing on large-scale
datasets and arbitrary performance metrics. By leveraging Bayesian optimization
(BO) with a surrogate model, our approach enables efficient hyperparameter
selection with multi-objective trade-offs and allows us to perform data-driven
sensitivity analysis. By incorporating normalization and subsampling, the
proposed framework demonstrates versatility and efficiency, as shown in
applications to visualization techniques such as t-SNE and UMAP. We evaluate
our results on various synthetic and real-world datasets using multiple quality
metrics, providing a robust and efficient solution for hyperparameter selection
in DR algorithms.

本研究提出了一个有效且强健的自动调参框架，用于在大型数据集和任意性能指标下进行降维算法的超参数选择，利用贝叶斯优化和代理模型，该方法使得超参数选择具有多目标权衡的效率，并允许我们进行数据驱动的敏感性分析。通过添加归一化和子采样，该框架表现出了多样性和高效性，并在 t-SNE 和 UMAP 等可视化技术应用中进行了评估。我们利用多种质量指标在各种合成和实际数据集上评估了我们的结果，从而为降维算法中的超参数选择提供了一个强健而高效的解决方案

高效稳健的贝叶斯超参数选择方法在降维可视化中的应用

Efficient and Robust Bayesian Selection of Hyperparameters in Dimension  Reduction for Visualization

High dimensional parameter space optimization is crucial in many
applications. The parameters affecting this performance can be both numerical
and categorical in their type. The existing techniques of black-box
optimization and visual analytics are good in dealing with numerical parameters
but analyzing categorical variables in context of the numerical variables are
not well studied. Hence, we propose a novel approach, to create an auto-tuning
framework for storage systems optimization combining both direct optimization
techniques and visual analytics research. While the optimization algorithm will
be the core of the system, visual analytics will provide a guideline with the
help of an external agent (expert) to provide crucial hints to narrow down the
large search space for the optimization engine. As part of the initial step
towards creating an auto-tuning engine for storage systems optimization, we
created an Interactive Configuration Explorer \textit{ICE}, which directly
addresses the need of analysts to learn how the dependent numerical variable is
affected by the parameter settings given multiple optimization objectives. No
information is lost as ICE shows the complete distribution and statistics of
the dependent variable in context with each categorical variable. Analysts can
interactively filter the variables to optimize for certain goals such as
achieving a system with maximum performance, low variance, etc. Our system was
developed in tight collaboration with a group of systems performance
researchers and its final effectiveness was evaluated with expert interviews, a
comparative user study, and two case studies. We also discuss our research plan
for creating an efficient auto-tuning framework combining black-box
optimization and visual analytics for storage systems performance optimization.

研究提出了一种结合黑盒优化和视觉分析的自动调优框架，用于储存系统性能优化，其中包括交互式配置浏览器 ICE，用于与类别变量上下文联合显示依赖数值变量的完整分布和统计信息。