| Reconstructing training data from trained neural networks N Haim, G Vardi, G Yehudai, O Shamir, M Irani Advances in Neural Information Processing Systems 35, 22911-22924, 2022 | 92 | 2022 |
| On the implicit bias in deep-learning algorithms G Vardi Communications of the ACM 66 (6), 86-93, 2023 | 50 | 2023 |
| Implicit regularization in relu networks with the square loss G Vardi, O Shamir Conference on Learning Theory, 4224-4258, 2021 | 45 | 2021 |
| Implicit regularization towards rank minimization in relu networks N Timor, G Vardi, O Shamir International Conference on Algorithmic Learning Theory, 1429-1459, 2023 | 36 | 2023 |
| Implicit bias in leaky relu networks trained on high-dimensional data S Frei, G Vardi, PL Bartlett, N Srebro, W Hu arXiv preprint arXiv:2210.07082, 2022 | 27 | 2022 |
| From local pseudorandom generators to hardness of learning A Daniely, G Vardi Conference on Learning Theory, 1358-1394, 2021 | 27 | 2021 |
| On margin maximization in linear and relu networks G Vardi, O Shamir, N Srebro Advances in Neural Information Processing Systems 35, 37024-37036, 2022 | 25 | 2022 |
| Neural networks with small weights and depth-separation barriers G Vardi, O Shamir Advances in neural information processing systems 33, 19433-19442, 2020 | 23 | 2020 |
| On the effective number of linear regions in shallow univariate relu networks: Convergence guarantees and implicit bias I Safran, G Vardi, JD Lee Advances in Neural Information Processing Systems 35, 32667-32679, 2022 | 22 | 2022 |
| Gradient methods provably converge to non-robust networks G Vardi, G Yehudai, O Shamir Advances in Neural Information Processing Systems 35, 20921-20932, 2022 | 21 | 2022 |
| Hardness of learning neural networks with natural weights A Daniely, G Vardi Advances in Neural Information Processing Systems 33, 930-940, 2020 | 21 | 2020 |
| Size and depth separation in approximating benign functions with neural networks G Vardi, D Reichman, T Pitassi, O Shamir Conference on Learning Theory, 4195-4223, 2021 | 20* | 2021 |
| Learning a single neuron with bias using gradient descent G Vardi, G Yehudai, O Shamir Advances in Neural Information Processing Systems 34, 28690-28700, 2021 | 19 | 2021 |
| Benign overfitting in linear classifiers and leaky relu networks from kkt conditions for margin maximization S Frei, G Vardi, P Bartlett, N Srebro The Thirty Sixth Annual Conference on Learning Theory, 3173-3228, 2023 | 18 | 2023 |
| On the optimal memorization power of relu neural networks G Vardi, G Yehudai, O Shamir arXiv preprint arXiv:2110.03187, 2021 | 18 | 2021 |
| On convexity and linear mode connectivity in neural networks D Yunis, KK Patel, PHP Savarese, G Vardi, J Frankle, M Walter, K Livescu, ... OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022 | 11 | 2022 |
| Width is less important than depth in ReLU neural networks G Vardi, G Yehudai, O Shamir Conference on learning theory, 1249-1281, 2022 | 11 | 2022 |
| Flow games O Kupferman, G Vardi, MY Vardi 37th IARCS Annual Conference on Foundations of Software Technology and …, 2018 | 10 | 2018 |
| Benign overfitting and grokking in relu networks for xor cluster data Z Xu, Y Wang, S Frei, G Vardi, W Hu arXiv preprint arXiv:2310.02541, 2023 | 9 | 2023 |
| Eulerian paths with regular constraints O Kupferman, G Vardi 41st International Symposium on Mathematical Foundations of Computer Science …, 2016 | 9 | 2016 |
