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

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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.