Safe Semi-supervised Learning

Description: 

To use unlabeled data or not, that is the question. It is known that semi-supervised learning can be inferior to supervised learning if its model assumption is violated. Can we design semi-supervised algorithms which are provably robust to such failure? The challenge is to detect model assumption violation from limited labeled data, where semi-supervised learning is most useful.

CS Collaborators: 

Jerry Zhu

Campus Collaborators: 

Rob Nowak (Electrical & Computer Engineering), Grace Wahba (Statistics)

Funding: 

National Science Foundation

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