Steve Hanneke
Also known as Stephen Robert Hanneke
Steve Hanneke is a researcher in the theory of machine learning, focusing on how many training examples are sufficient for learning. He is an assistant professor of computer science at Purdue University, which he joined in 2021. His ICML 2007 paper, A Bound on the Label Complexity of Agnostic Active Learning, received an honorable mention in the ICML 2017 Test of Time Award. In 2025, a NeurIPS Best Paper runner-up award went to his paper Optimal Mistake Bounds for Transductive Online Learning, written with three co-authors. After a 2009 PhD at Carnegie Mellon University, he held posts at Carnegie Mellon, Princeton, and the Toyota Technological Institute at Chicago before moving to Purdue.
Timeline
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2002–2005
Undergraduate study at Illinois
He studies computer science at the University of Illinois at Urbana-Champaign from 2002 to 2005. There he works on semi-supervised learning with Dan Roth.
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2009
A PhD in machine learning
He receives a PhD in 2009 from the Machine Learning Department at Carnegie Mellon University.
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2009–2012
Visiting assistant professor at Carnegie Mellon
He works as a visiting assistant professor in the statistics department at Carnegie Mellon from 2009 to 2012.
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2012–2018
Independent scientist at Princeton
He works as an independent scientist at Princeton University from 2012 to 2018.
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2017
Honorable mention for a 2007 paper
The ICML 2017 Test of Time Award gives an honorable mention to his 2007 ICML paper, A Bound on the Label Complexity of Agnostic Active Learning.
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2018–2021
Research assistant professor at TTIC
He works as a research assistant professor at the Toyota Technological Institute at Chicago from 2018 to 2021.
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2025
A NeurIPS runner-up award
He co-writes Optimal Mistake Bounds for Transductive Online Learning with Zachary Chase, Shay Moran, and Jonathan Shafer. The paper resolves a 30-year-old open problem about the power of unlabeled data in online learning and receives a NeurIPS 2025 Best Paper runner-up award.
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Since 2021
Assistant professor at Purdue
He is an assistant professor of computer science at Purdue University, a post he takes up in 2021. His research covers the theory of machine learning, with a focus on reducing the number of training examples sufficient for learning.
Sources and credits
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