SHF: Medium: Program Synthesis for Weak Supervision

人工智能，以机器学习的形式，在自动化困难的任务和在许多问题领域（包括语言，视觉和建议）中提取见解方面具有变革性，并提供了许多进一步的可能性。例如，机器学习有望自动分析医学图像，这是以前留给人类专家的任务。然而，机器学习算法通常需要从大量手工标记的数据中学习。手动标记数据是一个昂贵的人力密集型过程。该项目旨在从根本上减少机器学习所需的标记数据量，目标是将机器学习廉价、快速地应用于新的重要领域。该项目利用程序综合和弱监督技术来最小化构建高性能模型所需的标记数据量。弱监督用一些不精确的来源代替手标签，为监督训练提供粗略的信号。这样的资源是通过标记函数来表示的：这些小而粗糙的程序对手头任务的知识进行编码。该项目的目标是使用程序合成自动生成标记函数，消除手动编写标记函数和对编程专业知识的需求。该项目开发了标签功能的通用语言，并探索了综合功能的高效重用和更丰富的用户交互手段，以进一步降低标签需求。这个项目正在培养一群不同的学生。此外，pi正在设计一门关于机器学习的新课程，使用较少的标记数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial intelligence, in the form of machine learning, has been transformative in automating difficult tasks and extracting insights in numerous problem domains, including language, vision, and recommendations, and offers many further possibilities. For instance, machine learning holds the promise of automatically analyzing medical images, a task previously reserved for human specialists. However, machine-learning algorithms typically require learning from large amounts of hand-labeled data. Manually labeling data is an expensive and human-intensive process. This project seeks to radically minimize the amount of labeled data needed for machine learning, with the goal of enabling cheap and rapid application of machine learning to new and important domains.The project leverages program synthesis and weak supervision technology to minimize the amount of labeled data needed to build performant models. Weak supervision replaces hand labels with a number of imprecise sources providing a rough signal for supervised training. Such sources are expressed by labeling functions: small, rough programs that encode knowledge about the task at hand. The goal of this project is to have labeling functions be generated automatically using program synthesis, eliminating the manual writing of labeling functions and the need for programming expertise. The project develops a generic language of labeling functions, and explores efficient re-use of synthesized functions and richer means of user interaction to further reduce label requirements. The project is training a diverse group of students. Additionally, the PIs are designing a novel course on machine learning with less labeled data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.