| Globally optimal gradient descent for a convnet with gaussian inputs A Brutzkus, A Globerson International conference on machine learning, 605-614, 2017 | 335 | 2017 |
| Low latency privacy preserving inference A Brutzkus, R Gilad-Bachrach, O Elisha International Conference on Machine Learning, 812-821, 2019 | 305 | 2019 |
| SGD learns over-parameterized networks that provably generalize on linearly separable data A Brutzkus, A Globerson, E Malach, S Shalev-Shwartz arXiv preprint arXiv:1710.10174, 2017 | 301 | 2017 |
| Why do larger models generalize better? A theoretical perspective via the XOR problem A Brutzkus, A Globerson International Conference on Machine Learning, 822-830, 2019 | 98 | 2019 |
| A theoretical analysis of fine-tuning with linear teachers G Shachaf, A Brutzkus, A Globerson Advances in Neural Information Processing Systems 34, 15382-15394, 2021 | 28 | 2021 |
| Towards understanding learning in neural networks with linear teachers R Sarussi, A Brutzkus, A Globerson International Conference on Machine Learning, 9313-9322, 2021 | 25 | 2021 |
| ID3 learns juntas for smoothed product distributions A Brutzkus, A Daniely, E Malach Conference on Learning Theory, 902-915, 2020 | 21 | 2020 |
| An Optimization and Generalization Analysis for Max-Pooling Networks A Brutzkus, A Globerson arXiv preprint arXiv:2002.09781, 2020 | 21 | 2020 |
| Detecting domain name system (DNS) tunneling based on DNS logs and network data A Brutzkus, R Levin US Patent 10,412,107, 2019 | 13 | 2019 |
| On the optimality of trees generated by id3 A Brutzkus, A Daniely, E Malach arXiv preprint arXiv:1907.05444, 2019 | 13 | 2019 |
| Efficient learning of cnns using patch based features A Brutzkus, A Globerson, E Malach, AR Netser, S Shalev-Schwartz International Conference on Machine Learning, 2336-2356, 2022 | 7 | 2022 |
| On the inductive bias of neural networks for learning read-once dnfs I Bronstein, A Brutzkus, A Globerson Uncertainty in Artificial Intelligence, 255-265, 2022 | 4 | 2022 |
| How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers G Buzaglo, I Harel, MS Nacson, A Brutzkus, N Srebro, D Soudry arXiv preprint arXiv:2402.06323, 2024 | 3 | 2024 |
| Truth tellers and liars with fewer questions G Braunschvig, A Brutzkus, D Peleg, A Sealfon Discrete Mathematics 338 (8), 1310-1316, 2015 | | 2015 |
| Supplementary Material: A Theoretical Analysis of Fine-tuning with Linear Teachers G Shachaf, A Brutzkus, A Globerson | | |