Semi-Supervised Learning for Non-Experts

In machine learning, there has been significant interest in semi-supervised learning which learns from labeled and unlabeled data. The objective of this project is to make semi-supervised machine learning usable by non-experts on realistic tasks. The project is not narrowly focus on creating yet another semi-supervised learning model. Rather, it aims to utilize existing and future semi-supervised learning models in a manner that guarantees their applicability and success on real problems.

Publications from this project

1. Andrew Goldberg, Xiaojin Zhu, Alex Furger, and Jun-Ming Xu. OASIS: Online active semisupervised learning. In The Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), 2011. [pdf]


2. Xiaojin Zhu, Bryan Gibson, and Timothy Rogers. Co-training as a human collaboration policy. In The Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), 2011. [pdf]


3. Charles W. Kalish, Timothy T. Rogers, Jonathan Lang, and Xiaojin Zhu. Can semi-supervised learning explain incorrect beliefs about categories? Cognition, 2011. [link]


4. Andrew Goldberg, Xiaojin Zhu, Benjamin Recht, Jun-Ming Xu, and Robert Nowak. Transduction with matrix completion: Three birds with one stone. In Advances in Neural Information Processing Systems (NIPS) 24. 2010. [pdf]


5. Bryan Gibson, Xiaojin Zhu, Tim Rogers, Chuck Kalish, and Joseph Harrison. Humans learn using manifolds, reluctantly. In Advances in Neural Information Processing Systems (NIPS) 24, 2010. [pdf | NIPS talk slides]


6. Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, and Chuck Kalish. Cognitive models of test-item effects in human category learning. In The 27th International Conference on Machine Learning (ICML), 2010. [paper pdf]


7. Timothy Rogers, Charles Kalish, Bryan Gibson, Joseph Harrison, and Xiaojin Zhu. Semi-supervised learning is observed in a speeded but not an unspeeded 2D categorization task. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society (CogSci), 2010.
   [paper pdf]

Faculty

• Xiaojin Zhu (jerryzhu@cs.wisc.edu)

Graduate Students

• Bryan R. Gibson
• Andrew B. Goldberg
• Kwang-Sung Jun
• Yimin Tan
• Jun-Ming Xu

Undergraduate Students

• Alex Furger

Collaborators

• Professor Charles W. Kalish, Department of Educational Psychology, University of Wisconsin-Madison. Machine learning models applied to cognitive psychology.


• Professor Robert Nowak, Department of Electrical and Computer Engineering, University of Wisconsin-Madison. Optimization techniques for machine learning.


• Professor Benjamin Recht, Department of Computer Sciences, University of Wisconsin-Madison. Optimization techniques for machine learning.


• Professor Timothy T. Rogers, Department of Psychology, University of Wisconsin-Madison. Machine learning models applied to cognitive psychology.

This project is based upon work supported by the National Science Foundation under Grant No. IIS-0916038. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.