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. Charles Kalish, Xiaojin Zhu, and Timothy Rogers. Drift in children's categories: When experienced distributions conflict with prior learning. In Developmental Science, 2014.
   


2. Xiaojin Zhu. Machine teaching for Bayesian learners in the exponential family. In Advances in Neural Information Processing Systems (NIPS), 2013.
   We study machine teaching, or optimal teaching, the inverse problem of machine learning.
   [pdf | poster]


3. Bryan R. Gibson, Timothy T. Rogers, and Xiaojin Zhu. Human semi-supervised learning. Topics in Cognitive Science, 5(1):132-172, 2013.
   [link | data]


4. Kwang-Sung Jun, Xiaojin Zhu, Burr Settles, and Timothy Rogers. Learning from Human-Generated Lists. In The 30th International Conference on Machine Learning (ICML), 2013.
   Quick! Say as many animals as you can think of in 60 seconds without repetition! The list you produce is non-iid, non-exchangeable, and carries important information about animals (and your brain).
   [pdf | slides | SWIRL v1.0 code | video]


5. Xiaojin Zhu. Persistent homology: An introduction and a new text representation for natural language processing. In The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.
   A gentle tutorial on homology, and an application in machine learning.
   [pdf | slides (long, short) | poster | data and code ]


6. Nick Bridle and Xiaojin Zhu. p-voltages: Laplacian regularization for semi-supervised learning on high-dimensional data. In Eleventh Workshop on Mining and Learning with Graphs (MLG2013), 2013.
   [pdf (errata) | slides | poster | code]


7. Yimin Tan and Xiaojin Zhu. Dragging: Density-ratio bagging. Technical Report Computer Science TR1795, University of Wisconsin-Madison, 2013.
   [pdf]


8. Shilin Ding, Grace Wahba, and Xiaojin Zhu. Learning higher-order graph structure with features by structure penalty. In Advances in Neural Information Processing Systems (NIPS) 25. 2011.
   [pdf]


9. Faisal Khan, Xiaojin Zhu, and Bilge Mutlu. How do humans teach: On curriculum learning and teaching dimension. In Advances in Neural Information Processing Systems (NIPS) 25. 2011.
   [pdf | data | slides | UCSD teaching workshop talk]


10. Jun-Ming Xu, Xiaojin Zhu, and Timothy T. Rogers. Metric learning for estimating psychological similarities. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2011.
    [journal link | unofficial version | data | code]


11. 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]


12. 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]


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


14. 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]


15. 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]


16. 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]


17. 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]

Semi-Supervised Learning Software

• Web collection

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
• Jake Yang

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.