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Do not falter or shrink; But just think out your work, And just work out your think. -- Nixon Waterman --

Note: THIS WEBSITE IS OUT-DATED. I have now moved to a Visiting Assistant Professor position in the Department of Statistics at Carnegie Mellon University. My new webpage is http://www.stat.cmu.edu/~shanneke/. You should be automatically forwarded, or you can manually go there by clicking here.

My general research interest is in artificial intelligence; specifically, I am interested in systems that can improve their performance with experience, a problem known as machine learning.

My focus is on the informational complexity of machine learning. The essential questions I am interested in answering are "what can be learned from empirical observation and/or interaction," and "how much observation and/or interaction is necessary to learn it?" This overall topic intersects with several academic disciplines, including statistical inference, learning theory, algorithmic and statistical information theories, philosophy of science, and epistemology.

I also do some work on statistical modeling of social networks.

Unpublished Notes:

I also have some ``working notes'' that may be of interest to some people. These include unpublished results, which will eventually make it into my thesis, and possibly other publications in some cases. These notes are subject to frequent updates and changes -- mostly additions -- as I continue to explore these topics.

Rates of Convergence in Active Learning. (includes a survey of recent advances in studying rates of convergence in agnostic active learning, along with some new results).

A Refined Analysis of PAC Learning Via the Disagreement Coefficient. (the disagreement coefficient turns out to be useful for studying passive learning too).

Activized Learning: Transforming Passive to Active with Improved Label Complexity. (see thesis chapter 4)[slides][video]

Publications: (authors are listed in alphabetical order).

2009

Hanneke, S. (2009). Theoretical Foundations of Active Learning. Doctoral Dissertation. Machine Learning Department. Carnegie Mellon University. [pdf][ps][defense slides]

Hanneke, S. (2009). Adaptive Rates of Convergence in Active Learning. In proceedings of the 22nd Annual Conference on Learning Theory (COLT).[pdf][ps][ppt]

Hanneke, S., and Xing, E.P. (2009). Network Completion and Survey Sampling. In proceedings of the 12th International Conference on Artificial Intelligence and Statistics.[pdf][ps][slides]

2008

Balcan, M.-F., Hanneke, S., Wortman, J. (2008). The True Sample Complexity of Active Learning. In proceedings of the 21st Annual Conference on Learning Theory (COLT). [pdf][ps][slides]
Winner of the Mark Fulk Best Student Paper Award.

2007

Balcan, M.-F., Even-Dar, E., Hanneke, S., Kearns, M., Mansour, Y., Wortman, J. (2007). Asymptotic Active Learning. NIPS Workshop on Principles of Learning Problem Design. [pdf][ps] [spotlight slide]

Hanneke, S., and Xing, E.P. (2007). Network Completion and Survey Sampling. NIPS Workshop on Statistical Network Models.

Hanneke, S. (2007). Teaching Dimension and the Complexity of Active Learning. In proceedings of the 20th Annual Conference on Learning Theory (COLT). [pdf][ps][slides ppt]

Hanneke, S. (2007). A Bound on the Label Complexity of Agnostic Active Learning. In proceedings of the 24th Annual International Conference on Machine Learning (ICML). [pdf][ps][slides ppt]

Fu, W., Guo, F., Hanneke, S., and Xing, E.P. (2007). Recovering Temporally Rewiring Networks: A Model-based Approach. In proceedings of the 24th Annual International Conference on Machine Learning (ICML). [pdf]
Also see our related earlier work.

Hanneke, S. (2007). The Complexity of Interactive Machine Learning. KDD Project Report (aka Master's Thesis). Machine Learning Department, Carnegie Mellon University. [pdf] [ps] [slides ppt]
Includes some interesting results from a class project on The Cost Complexity of Interactive Learning, in addition to my COLT07 and ICML07 papers.

2006

Hanneke, S. and Xing, E.P. (2006). Discrete Temporal Models of Social Networks. In proceedings of the ICML Workshop on Statistical Network Analysis. [pdf][ps][slides]

Hanneke, S. (2006). An Analysis of Graph Cut Size for Transductive Learning. In proceedings of the 23rd International Conference on Machine Learning (ICML). [pdf][ps][slides ppt][slides pdf]

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