**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]

**Unpublished Notes:**

I have some "working notes" that may be of interest to some people.
These include unpublished results, which may eventually become papers.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.

*A Statistical 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 this survey as this topic continues to develop;
the current version (v0.0) was updated on March 6, 2013.

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

**Publications:**

**2013**

Hanneke, S. and Yang, L. (2013).
*Activized Learning with Uniform Classification Noise*.
In Proceedings of the 30^{th} 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 25^{th} 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 24^{th} 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 14^{th} 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 21^{st} 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 13^{th} 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 22^{nd} 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 12^{th} 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 21^{st} 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 20^{th} 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 24^{th} 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 24^{th} 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 23^{rd} International Conference on Machine Learning (ICML).
[pdf][ps][slides ppt][slides pdf]