Steve Hanneke
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Discrete temporal models of social networks
S Hanneke, W Fu, EP Xing
Electronic Journal of Statistics 4, 585-605, 2010
4152010
A bound on the label complexity of agnostic active learning
S Hanneke
Proceedings of the 24th international conference on Machine learning, 353-360, 2007
2912007
The true sample complexity of active learning
MF Balcan, S Hanneke, JW Vaughan
Machine learning 80 (2-3), 111-139, 2010
1792010
Theory of disagreement-based active learning
S Hanneke
Foundations and Trends® in Machine Learning 7 (2-3), 131-309, 2014
1482014
Recovering temporally rewiring networks: A model-based approach
F Guo, S Hanneke, W Fu, EP Xing
Proceedings of the 24th international conference on Machine learning, 321-328, 2007
1332007
Rates of convergence in active learning
S Hanneke
The Annals of Statistics 39 (1), 333-361, 2011
1292011
Theoretical foundations of active learning
S Hanneke
CARNEGIE-MELLON UNIV PITTSBURGH PA MACHINE LEARNING DEPT, 2009
1012009
Discrete temporal models of social networks
S Hanneke, EP Xing
ICML Workshop on Statistical Network Analysis, 115-125, 2006
962006
Teaching dimension and the complexity of active learning
S Hanneke
International Conference on Computational Learning Theory, 66-81, 2007
922007
A theory of transfer learning with applications to active learning
L Yang, S Hanneke, J Carbonell
Machine learning 90 (2), 161-189, 2013
822013
The optimal sample complexity of PAC learning
S Hanneke
The Journal of Machine Learning Research 17 (1), 1319-1333, 2016
762016
Activized learning: Transforming passive to active with improved label complexity
S Hanneke
The Journal of Machine Learning Research 13 (1), 1469-1587, 2012
542012
Minimax analysis of active learning
S Hanneke, L Yang
The Journal of Machine Learning Research 16 (1), 3487-3602, 2015
532015
Adaptive Rates of Convergence in Active Learning.
S Hanneke
COLT, 2009
482009
Network completion and survey sampling
S Hanneke, EP Xing
Artificial Intelligence and Statistics, 209-215, 2009
412009
VC classes are adversarially robustly learnable, but only improperly
O Montasser, S Hanneke, N Srebro
arXiv preprint arXiv:1902.04217, 2019
352019
Robust interactive learning
MF Balcan, S Hanneke
Conference on Learning Theory, 20.1-20.34, 2012
352012
Surrogate losses in passive and active learning
S Hanneke, L Yang
Electronic Journal of Statistics 13 (2), 4646-4708, 2019
292019
An analysis of graph cut size for transductive learning
S Hanneke
Proceedings of the 23rd international conference on Machine learning, 393-399, 2006
212006
Refined error bounds for several learning algorithms
S Hanneke
The Journal of Machine Learning Research 17 (1), 4667-4721, 2016
192016
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