| Discrete temporal models of social networks S Hanneke, W Fu, EP Xing | 623 | 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 | 358 | 2007 |
| Theory of disagreement-based active learning S Hanneke Foundations and Trends® in Machine Learning 7 (2-3), 131-309, 2014 | 350 | 2014 |
| The true sample complexity of active learning MF Balcan, S Hanneke, JW Vaughan Machine learning 80, 111-139, 2010 | 210 | 2010 |
| A theory of transfer learning with applications to active learning L Yang, S Hanneke, J Carbonell Machine learning 90, 161-189, 2013 | 190 | 2013 |
| Rates of convergence in active learning S Hanneke The Annals of Statistics, 333-361, 2011 | 170 | 2011 |
| The optimal sample complexity of PAC learning S Hanneke Journal of Machine Learning Research 17 (38), 1-15, 2016 | 163 | 2016 |
| Vc classes are adversarially robustly learnable, but only improperly O Montasser, S Hanneke, N Srebro Conference on Learning Theory, 2512-2530, 2019 | 155 | 2019 |
| 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 | 146 | 2007 |
| Theoretical foundations of active learning S Hanneke Carnegie Mellon University, 2009 | 129 | 2009 |
| Minimax analysis of active learning. S Hanneke, L Yang J. Mach. Learn. Res. 16 (1), 3487-3602, 2015 | 126 | 2015 |
| Discrete temporal models of social networks S Hanneke, EP Xing ICML Workshop on Statistical Network Analysis, 115-125, 2006 | 113 | 2006 |
| Teaching dimension and the complexity of active learning S Hanneke International conference on computational learning theory, 66-81, 2007 | 101 | 2007 |
| On the value of target data in transfer learning S Hanneke, S Kpotufe Advances in Neural Information Processing Systems 32, 2019 | 84 | 2019 |
| Activized learning: Transforming passive to active with improved label complexity S Hanneke The Journal of Machine Learning Research 13 (1), 1469-1587, 2012 | 66 | 2012 |
| A theory of universal learning O Bousquet, S Hanneke, S Moran, R Van Handel, A Yehudayoff Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 56 | 2021 |
| Universal Bayes consistency in metric spaces S Hanneke, A Kontorovich, S Sabato, R Weiss 2020 Information Theory and Applications Workshop (ITA), 1-33, 2020 | 56 | 2020 |
| Adaptive Rates of Convergence in Active Learning. S Hanneke COLT, 2009 | 56 | 2009 |
| Network completion and survey sampling S Hanneke, EP Xing Artificial intelligence and statistics, 209-215, 2009 | 53 | 2009 |
| Proper learning, Helly number, and an optimal SVM bound O Bousquet, S Hanneke, S Moran, N Zhivotovskiy Conference on Learning Theory, 582-609, 2020 | 52 | 2020 |
