| Discrete temporal models of social networks S Hanneke, W Fu, EP Xing Electronic journal of statistics 4, 585-605, 2010 | 424 | 2010 |
| A bound on the label complexity of agnostic active learning S Hanneke Proceedings of the 24th international conference on Machine learning, 353-360, 2007 | 299 | 2007 |
| The true sample complexity of active learning MF Balcan, S Hanneke, JW Vaughan Machine learning 80 (2-3), 111-139, 2010 | 187 | 2010 |
| Theory of disagreement-based active learning S Hanneke Foundations and TrendsŪ in Machine Learning 7 (2-3), 131-309, 2014 | 160 | 2014 |
| 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 | 135 | 2007 |
| Rates of convergence in active learning S Hanneke The Annals of Statistics 39 (1), 333-361, 2011 | 133 | 2011 |
| Theoretical foundations of active learning S Hanneke CARNEGIE-MELLON UNIV PITTSBURGH PA MACHINE LEARNING DEPT, 2009 | 107 | 2009 |
| Discrete temporal models of social networks S Hanneke, EP Xing ICML Workshop on Statistical Network Analysis, 115-125, 2006 | 96 | 2006 |
| Teaching dimension and the complexity of active learning S Hanneke International Conference on Computational Learning Theory, 66-81, 2007 | 92 | 2007 |
| The optimal sample complexity of PAC learning S Hanneke The Journal of Machine Learning Research 17 (1), 1319-1333, 2016 | 88 | 2016 |
| A theory of transfer learning with applications to active learning L Yang, S Hanneke, J Carbonell Machine learning 90 (2), 161-189, 2013 | 86 | 2013 |
| Minimax analysis of active learning. S Hanneke, L Yang J. Mach. Learn. Res. 16 (12), 3487-3602, 2015 | 60 | 2015 |
| Activized learning: Transforming passive to active with improved label complexity S Hanneke The Journal of Machine Learning Research 13 (1), 1469-1587, 2012 | 55 | 2012 |
| Adaptive Rates of Convergence in Active Learning. S Hanneke COLT, 2009 | 47 | 2009 |
| Network completion and survey sampling S Hanneke, EP Xing Artificial Intelligence and Statistics, 209-215, 2009 | 45 | 2009 |
| VC classes are adversarially robustly learnable, but only improperly O Montasser, S Hanneke, N Srebro Conference on Learning Theory, 2512-2530, 2019 | 41 | 2019 |
| Robust interactive learning MF Balcan, S Hanneke Conference on Learning Theory, 20.1-20.34, 2012 | 37 | 2012 |
| Surrogate losses in passive and active learning S Hanneke, L Yang Electronic Journal of Statistics 13 (2), 4646-4708, 2019 | 30 | 2019 |
| Theory of active learning S Hanneke Foundations and Trends in Machine Learning 7 (2-3), 2014 | 27 | 2014 |
| Refined error bounds for several learning algorithms S Hanneke The Journal of Machine Learning Research 17 (1), 4667-4721, 2016 | 21 | 2016 |
