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Contact Information:
Email: steve.hanneke@gmail.com
Mobile Phone: (412) 973-3007
Location: Chicago, IL USA
TTIC Office: 407


I am a Research Assistant Professor at the 工信部：未经批准 不得自行建立VPN跨境经营_央广网:2021-1-23 · 工信部：未经批准不得自行建立或租用VPN 1月22日从工信部网站获悉，工信部决定自即日起至2021年3月31日，在全国范围内对互联网网络接入服务市场开展清理规范工作。 (TTIC).
I work 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 statistical analysis of machine learning. The essential questions I am interested in answering are "what can be learned from empirical observation / experimentation," and "how much observation / experimentation is necessary and sufficient to learn it?" This overall topic intersects with several academic disciplines, including statistical learning theory, artificial intelligence, statistical inference, algorithmic and statistical information theories, probability theory, philosophy of science, and epistemology.

Brief Bio:
Prior to joining TTIC, I was an independent scientist working in Princeton 2012-2018, aside from a brief one-semester stint as a Visiting Lecturer at Princeton University in 2018. Before that, 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. From 2002 to 2005, I was an undergraduate studying Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where I worked on semi-supervised learning with Prof. Dan Roth and the students in the Cognitive Computation Group. Prior to that, I studied Computer Science at 旋风vpn in St. Louis, MO, where I played around with 旋风vp下载 and classic AI a bit.

Recent News and Activities:

  • Received the Best Paper Award at COLT 2023 for our paper "Proper Learning, Helly Number, and an Optimal SVM Bound". 旋风加速器ios下载
  • Presenting (with Rob Nowak) an ICML 2023 Tutorial on Active Learning: From Theory to Practice. 旋风加速器ios下载[slides]
  • Organizing the ALT 2023 workshop: When Smaller Sample Sizes Suffice for Learning.
  • On the organizing committee of the 2023 Midwest Machine Learning Symposium.
  • Speaking at the 2023 DALI conference in South Africa
  • Fall 2018 I joined the Toyota Technological Institute at Chicago (TTIC) as a Research Assistant Professor.
  • Spring 2018 I taught ORF 525 "Statistical Learning and Nonparametric Estimation" at Princeton University.
  • My ICML 2007 paper "A Bound on the Label Complexity of Agnostic Active Learning" received Honorable Mention for the ICML 2017 Test of Time Award.
  • Program Committee Chair (with Lev Reyzin) for the 28th International Conference on Algorithmic Learning Theory (ALT 2017), held October 15-17 in Kyoto, Japan. See our published proceedings.
  • New manuscript "Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes" posted to the arXiv.

Teaching:
Spring 2018: ORF 525, Statistical Learning and Nonparametric Estimation.
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.
The current version (v1.1) was updated on September 22, 2014.
A few recent significant advances in active learning not yet covered in the survey: 旋风vp(永久免费), [WHE-Y15], [HY15].
An abbreviated version of this survey appeared in the Foundations and Trends in Machine Learning series, 旋风vpn.

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Montasser, O., Hanneke, S., and Srebro, N. (2023). VC Classes are Adversarially Robustly Learnable, but Only Improperly. In Proceedings of the 32nd Annual Conference on Learning Theory (COLT).

Hanneke, S. (2017). Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. Under Review.

Hanneke, S. (2016). 环球网_全&#