| Estimating information flow in deep neural networks Z Goldfeld, E Berg, K Greenewald, I Melnyk, N Nguyen, B Kingsbury, ... arXiv preprint arXiv:1810.05728, 2018 | 165 | 2018 |
| The information bottleneck problem and its applications in machine learning Z Goldfeld, Y Polyanskiy IEEE Journal on Selected Areas in Information Theory 1 (1), 19-38, 2020 | 95 | 2020 |
| Semantic-security capacity for wiretap channels of type II Z Goldfeld, P Cuff, HH Permuter IEEE Transactions on Information Theory 62 (7), 3863-3879, 2016 | 69 | 2016 |
| Convergence of smoothed empirical measures with applications to entropy estimation Z Goldfeld, K Greenewald, J Niles-Weed, Y Polyanskiy arXiv preprint arXiv:1905.13576, 2019 | 68* | 2019 |
| Arbitrarily varying wiretap channels with type constrained states Z Goldfeld, P Cuff, HH Permuter IEEE Transactions on Information Theory 62 (12), 7216-7244, 2016 | 41 | 2016 |
| Wiretap channels with random states non-causally available at the encoder Z Goldfeld, P Cuff, HH Permuter IEEE Transactions on Information Theory 66 (3), 1497-1519, 2019 | 32 | 2019 |
| Fourier-Motzkin Elimination Software for Information Theoretic Inequalities IB Gattegno, Z Goldfeld, HH Permuter arXiv preprint arXiv:1610.03990, 2016 | 28* | 2016 |
| Neural estimation of statistical divergences S Sreekumar, Z Goldfeld The Journal of Machine Learning Research 23 (1), 5460-5534, 2022 | 26 | 2022 |
| Sliced mutual information: A scalable measure of statistical dependence Z Goldfeld, K Greenewald Advances in Neural Information Processing Systems 34, 17567-17578, 2021 | 26 | 2021 |
| Gaussian-smoothed optimal transport: Metric structure and statistical efficiency Z Goldfeld, K Greenewald International Conference on Artificial Intelligence and Statistics, 3327-3337, 2020 | 25 | 2020 |
| Capacity of continuous channels with memory via directed information neural estimator Z Aharoni, D Tsur, Z Goldfeld, HH Permuter 2020 IEEE International Symposium on Information Theory (ISIT), 2014-2019, 2020 | 22 | 2020 |
| Smooth -Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications S Nietert, Z Goldfeld, K Kato International Conference on Machine Learning, 8172-8183, 2021 | 21 | 2021 |
| Asymptotic guarantees for generative modeling based on the smooth Wasserstein distance Z Goldfeld, K Greenewald, K Kato Advances in neural information processing systems 33, 2527-2539, 2020 | 21* | 2020 |
| Strong secrecy for cooperative broadcast channels Z Goldfeld, G Kramer, HH Permuter, P Cuff IEEE Trans. Information Theory 63 (1), 469-495, 2017 | 20 | 2017 |
| Statistical inference with regularized optimal transport Z Goldfeld, K Kato, G Rioux, R Sadhu arXiv preprint arXiv:2205.04283, 2022 | 19 | 2022 |
| MIMO Gaussian broadcast channels with common, private, and confidential messages Z Goldfeld, HH Permuter IEEE Transactions on Information Theory 65 (4), 2525-2544, 2019 | 17 | 2019 |
| Statistical, robustness, and computational guarantees for sliced wasserstein distances S Nietert, Z Goldfeld, R Sadhu, K Kato Advances in Neural Information Processing Systems 35, 28179-28193, 2022 | 16 | 2022 |
| Non-asymptotic performance guarantees for neural estimation of f-divergences S Sreekumar, Z Zhang, Z Goldfeld International Conference on Artificial Intelligence and Statistics, 3322-3330, 2021 | 15 | 2021 |
| Key and message semantic-security over state-dependent channels A Bunin, Z Goldfeld, HH Permuter, S Shamai, P Cuff, P Piantanida IEEE Transactions on Information Forensics and Security, 2018 | 15 | 2018 |
| Outlier-robust optimal transport: Duality, structure, and statistical analysis S Nietert, Z Goldfeld, R Cummings International Conference on Artificial Intelligence and Statistics, 11691-11719, 2022 | 14 | 2022 |
Haim H. PermuterBen Gurion UniversityVerified email at bgu.ac.il
