| Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity A Daniely, R Frostig, Y Singer Advances In Neural Information Processing Systems 29, 2253-2261, 2016 | 230 | 2016 |
| SGD learns the conjugate kernel class of the network A Daniely arXiv preprint arXiv:1702.08503, 2017 | 121 | 2017 |
| Complexity theoretic limitations on learning halfspaces A Daniely Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 102 | 2016 |
| Complexity theoretic limitations on learning DNF’s A Daniely, S Shalev-Shwartz Conference on Learning Theory, 815-830, 2016 | 101 | 2016 |
| Strongly adaptive online learning A Daniely, A Gonen, S Shalev-Shwartz International Conference on Machine Learning, 1405-1411, 2015 | 90 | 2015 |
| From average case complexity to improper learning complexity A Daniely, N Linial, S Shalev-Shwartz Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 87 | 2014 |
| Multiclass learnability and the erm principle A Daniely, S Sabato, S Ben-David, S Shalev-Shwartz Proceedings of the 24th Annual Conference on Learning Theory, 207-232, 2011 | 75 | 2011 |
| Depth separation for neural networks A Daniely Conference on Learning Theory, 690-696, 2017 | 44 | 2017 |
| More data speeds up training time in learning halfspaces over sparse vectors A Daniely, N Linial, SS Shwartz arXiv preprint arXiv:1311.2271, 2013 | 42 | 2013 |
| Clustering is difficult only when it does not matter A Daniely, N Linial, M Saks arXiv preprint arXiv:1205.4891, 2012 | 40 | 2012 |
| Optimal learners for multiclass problems A Daniely, S Shalev-Shwartz Conference on Learning Theory, 287-316, 2014 | 39 | 2014 |
| Multiclass learning approaches: A theoretical comparison with implications A Daniely, S Sabato, SS Shwartz arXiv preprint arXiv:1205.6432, 2012 | 39 | 2012 |
| A PTAS for agnostically learning halfspaces A Daniely Conference on Learning Theory, 484-502, 2015 | 38 | 2015 |
| On the practically interesting instances of MAXCUT Y Bilu, A Daniely, N Linial, M Saks arXiv preprint arXiv:1205.4893, 2012 | 37 | 2012 |
| Learning economic parameters from revealed preferences MF Balcan, A Daniely, R Mehta, R Urner, VV Vazirani International Conference on Web and Internet Economics, 338-353, 2014 | 34 | 2014 |
| Inapproximability of truthful mechanisms via generalizations of the VC dimension A Daniely, M Schapira, G Shahaf Proceedings of the forty-seventh annual ACM symposium on Theory of Computing …, 2015 | 19 | 2015 |
| Random features for compositional kernels A Daniely, R Frostig, V Gupta, Y Singer arXiv preprint arXiv:1703.07872, 2017 | 15 | 2017 |
| The complexity of learning halfspaces using generalized linear methods A Daniely, N Linial, S Shalev-Shwartz Conference on Learning Theory, 244-286, 2014 | 14 | 2014 |
| Locally private learning without interaction requires separation A Daniely, V Feldman arXiv preprint arXiv:1809.09165, 2018 | 12 | 2018 |
| Neural networks learning and memorization with (almost) no over-parameterization A Daniely arXiv preprint arXiv:1911.09873, 2019 | 11 | 2019 |
Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
