CACTUS：检测和解决目标函数中的冲突
CACTUS: Detecting and Resolving Conflicts in Objective Functions
摘要：
机器学习（ML）模型由专家ML从业者使用各种编码语言构建，其中他们为给定的问题域调整和选择模型超参数和学习算法。他们还仔细设计了一个目标函数或损失函数（通常具有多个目标），用于捕获给定ML任务（如分类，回归等）的期望输出。在这样的问题中，几个相互竞争的目标，没有一个单一的最佳解决方案，同时满足所有期望的目标。在过去的VA系统已经允许用户交互式地构建分类器的目标函数。在本文中，我们扩展了这条线的工作，原型技术可视化多目标的目标函数，无论是在一个笔记本电脑或定义使用交互式可视化界面，以帮助用户：（1）感知和解释复杂的数学术语，（2）检测和解决冲突的目标。目标函数的可视化启发了潜在冲突的目标，这些目标阻碍了为所需的ML任务或目标选择正确的解决方案。我们还提出了一个枚举的潜在冲突，在多目标目标函数的分类器选择的目标规范。此外，我们证明了我们的方法在VA系统，帮助用户在指定有意义的目标函数的分类器，通过检测和解决冲突的目标和约束。通过一个主题内的定量和定性的用户研究，我们提出的结果表明，我们的技术可以帮助用户交互式地指定有意义的目标函数，解决潜在的冲突的分类任务。
英文摘要：
Machine learning (ML) models are constructed by expert ML practitioners using various coding languages, in which they tune and select models hyperparameters and learning algorithms for a given problem domain. They also carefully design an objective function or loss function (often with multiple objectives) that captures the desired output for a given ML task such as classification, regression, etc. In multi-objective optimization, conflicting objectives and constraints is a major area of concern. In such problems, several competing objectives are seen for which no single optimal solution is found that satisfies all desired objectives simultaneously. In the past VA systems have allowed users to interactively construct objective functions for a classifier. In this paper, we extend this line of work by prototyping a technique to visualize multi-objective objective functions either defined in a Jupyter notebook or defined using an interactive visual interface to help users to: (1) perceive and interpret complex mathematical terms in it and (2) detect and resolve conflicting objectives. Visualization of the objective function enlightens potentially conflicting objectives that obstructs selecting correct solution(s) for the desired ML task or goal. We also present an enumeration of potential conflicts in objective specification in multi-objective objective functions for classifier selection. Furthermore, we demonstrate our approach in a VA system that helps users in specifying meaningful objective functions to a classifier by detecting and resolving conflicting objectives and constraints. Through a within-subject quantitative and qualitative user study, we present results showing that our technique helps users interactively specify meaningful objective functions by resolving potential conflicts for a classification task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
