RI: Small: Semi-Supervised Learning for Non-Experts

该项目开发实用的半监督机器学习算法，同时以严格的理论为指导。特别是，该项目正在开发学习理论，以量化何时以及在何种程度上标记和未标记数据的组合可证明是有益的。基于该理论，正在开发新的算法来解决目前阻碍半监督学习广泛采用的问题。新算法将能够保证使用未标记数据至少不会比监督学习更差，而且往往更好。新算法还将能够从无限量的监督和无监督数据中学习，因为它们是实时到达的，这是人类可以做到的，但到目前为止计算机还做不到。 该项目具有一些更广泛的影响：（1）开源软件将成为科学和技术新发现的支持工具，使机器学习在标记数据稀缺的情况下成为可能或更好。由于该软件专门针对非机器学习专家，因此预计其影响将遍及利用机器学习的整个科学和技术领域。(2)它通过新的机器学习理论推进了我们对学习过程的理解，这些理论可以应用于计算机和人类。(3)该提案包含非常适合让学生参与计算机科学教育和研究的项目。

This project develops semi-supervised machine learning algorithms that are practical, and at the same time guided by rigorous theory. In particular, the project is developing learning theory that quantifies when and to what extent the combination of labeled and unlabeled data is provably beneficial. Based on the theory, novel algorithms are being developed to address issues that currently hinder the wide adoption of semi-supervised learning. The new algorithms will be able to guarantee that using unlabeled data is at least no worse, and often better, than supervised learning. The new algorithms will also be able to learn from unlimited amounts of supervised and unsupervised data as they arrive in real-time, something humans can do but computers cannot so far. This project has a number of broader impacts: (1) An open-source software will be an enabling tool for new discoveries in science and technology, by making machine learning possible or better in situations where labeled data is scarce. Since the software specifically targets non-machine-learning-experts, the impact is expected to be across the whole spectrum of science and technology that utilizes machine learning. (2) It advances our understanding of the learning process via new machine learning theory, which can be applied to both computers and humans. (3) The proposal contains projects ideally suited to engage students in computer science education and research.