Steve Hanneke



Steve-Hanneke.JPG Contact Information:
Email: steve.hanneke@gmail.com
Mobile Phone: (412) 973-3007




I am an independent scientist, working on topics in statistical learning theory.

Research Interests:
My general research interest is in systems that can improve their performance with experience, a topic 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.

Brief Bio:
From 2009 to 2012, I was a Visiting Assistant Professor in the Department of Statistics at Carnegie Mellon University, also affiliated with the Machine Learning Department.
I received my PhD in 2009 from the Machine Learning Department at Carnegie Mellon University, co-advised by Eric Xing and Larry Wasserman. My thesis work was on the theoretical foundations of active learning. Prior to that, I was an undergraduate studying Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where I studied semi-supervised learning with Prof. Dan Roth and the students in the Cognitive Computation Group. Prior to that, I studied Computer Science at Webster University in St. Louis, MO, where I played around with neural networks and classic AI a bit.

Teaching:
Spring 2012: 36-752, Advanced Probability Overview.
Fall 2011: 36-755, Advanced Statistical Theory I.
Spring 2011: 36-752, Advanced Probability Overview.
Fall 2010 Mini 1: 36-781, Advanced Statistical Methods I: Active Learning
Fall 2010 Mini 2: 36-782, Advanced Statistical Methods II: Advanced Topics in Machine Learning Theory
Spring 2010: 36-754, Advanced Probability II: Stochastic Processes.
Fall 2009: 36-752, Advanced Probability Overview.
At ALT 2010 and the 2010 Machine Learning Summer School in Canberra, Australia, I gave a tutorial on the theory of active learning. [slides]


A Survey of Theoretical Active Learning:

Theory of Active Learning. [pdf][ps].

This is a survey of some of the recent advances in the theory of active learning, with particular emphasis on label complexity guarantees for disagreement-based methods.
Note: I will be updating and expanding this survey as this area continues to develop; the current version (v1.0) was updated on May 7, 2014.
An abbreviated version appeared in the Foundations and Trends in Machine Learning series, Volume 7, Issue 2-3, 2014.

Unpublished Notes:

I have some "working notes" that may be of interest to some people. These include articles under review and unpublished results, which may eventually become papers.
These notes are subject to frequent updates and changes -- mostly additions -- as I continue to explore these topics.

A Compression Technique for Analyzing Disagreement-Based Active Learning. [pdf][ps][arXiv]. Joint work with Ran El-Yaniv and Yair Wiener.

Surrogate Losses in Passive and Active Learning. [pdf][ps][arXiv]. Joint work with Liu Yang.

Publications: (authors are listed in alphabetical order).

2014

Hanneke, S. (2014). Theory of Disagreement-Based Active Learning. Foundations and Trends in Machine Learning. Vol. 7 (2-3), pp 131-309. [official] [Amazon]
There is also an extended version, which I update from time to time.

2013

Hanneke, S. and Yang, L. (2013). Activized Learning with Uniform Classification Noise. In Proceedings of the 30th International Conference on Machine Learning (ICML). [pdf][ps][appendix pdf][appendix ps]

Carbonell, J., Hanneke, S., and Yang, L. (2013). A Theory of Transfer Learning with Applications to Active Learning. Machine Learning, Vol. 90 (2), pp. 161-189. [pdf][ps][journal page]

2012

Balcan, M.-F. and Hanneke, S. (2012). Robust Interactive Learning. In Proceedings of the 25th Annual Conference on Learning Theory (COLT).[pdf][ps] [arXiv]

Hanneke, S. (2012). Activized Learning: Transforming Passive to Active with Improved Label Complexity. Journal of Machine Learning Research, Vol. 13 (5), pp. 1469-1587. [pdf] [ps] [arXiv] [journal page]
Related material: extended abstract, Chapter 4 in my thesis, my active learning class, and various presentations [slides][video].

2011

Carbonell, J., Hanneke, S., and Yang, L. (2011). Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. In Proceedings of the 24th Annual Conference on Learning Theory (COLT).[pdf][ps]

Carbonell, J., Hanneke, S., and Yang, L. (2011). The Sample Complexity of Self-Verifying Bayesian Active Learning. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS).[pdf][ps]

Hanneke, S. (2011). Rates of Convergence in Active Learning. The Annals of Statistics, Vol. 39 (1), pp. 333-361. [pdf][ps][journal page]

2010

Carbonell, J., Hanneke, S., and Yang, L. (2010). Bayesian Active Learning Using Arbitrary Binary Valued Queries. In Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT).[pdf][ps]
Also available in information theory jargon. [pdf][ps]

Fu, W., Hanneke, S., and Xing, E.P. (2010). Discrete Temporal Models of Social Networks. The Electronic Journal of Statistics, Vol. 4, pp. 585-605. [pdf]

Hanneke, S. and Yang, L. (2010). Negative Results for Active Learning with Convex Losses. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). [pdf] [ps]

Balcan, M.-F., Hanneke, S., and Wortman Vaughan, J. (2010). The True Sample Complexity of Active Learning. Machine Learning, Vol. 80 (2-3), pp. 111-139. [pdf][ps]

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][slides]
Also available in expanded journal version.

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

2008

Balcan, M.-F., Hanneke, S., and 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.
Also available in an extended journal version.

2007

Balcan, M.-F., Even-Dar, E., Hanneke, S., Kearns, M., Mansour, Y., and Wortman, J. (2007). Asymptotic Active Learning. NIPS Workshop on Principles of Learning Problem Design. [pdf][ps] [spotlight slide]
Also available in improved conference version and expanded journal version.

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

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]

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]

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]
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]
Also available in an extended journal version

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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