| Gans may have no nash equilibria F Farnia, A Ozdaglar arXiv preprint arXiv:2002.09124, 2020 | 192* | 2020 |
| Robust federated learning: The case of affine distribution shifts A Reisizadeh, F Farnia, R Pedarsani, A Jadbabaie Advances in neural information processing systems 33, 21554-21565, 2020 | 184 | 2020 |
| Generalizable adversarial training via spectral normalization F Farnia, JM Zhang, D Tse arXiv preprint arXiv:1811.07457, 2018 | 179 | 2018 |
| A minimax approach to supervised learning F Farnia, D Tse Advances in Neural Information Processing Systems 29, 2016 | 142 | 2016 |
| Understanding GANs in the LQG setting: Formulation, generalization and stability S Feizi, F Farnia, T Ginart, D Tse IEEE Journal on Selected Areas in Information Theory 1 (1), 304-311, 2020 | 113* | 2020 |
| Near optimal energy control and approximate capacity of energy harvesting communication Y Dong, F Farnia, A Özgür IEEE Journal on Selected Areas in Communications 33 (3), 540-557, 2015 | 98 | 2015 |
| A convex duality framework for GANs F Farnia, D Tse Advances in neural information processing systems 31, 2018 | 70 | 2018 |
| Train simultaneously, generalize better: Stability of gradient-based minimax learners F Farnia, A Ozdaglar International Conference on Machine Learning, 3174-3185, 2021 | 49 | 2021 |
| An optimal transport approach to personalized federated learning F Farnia, A Reisizadeh, R Pedarsani, A Jadbabaie IEEE Journal on Selected Areas in Information Theory 3 (2), 162-171, 2022 | 16 | 2022 |
| Gat–gmm: Generative adversarial training for gaussian mixture models F Farnia, WW Wang, S Das, A Jadbabaie SIAM Journal on Mathematics of Data Science 5 (1), 122-146, 2023 | 12 | 2023 |
| Discrete rényi classifiers M Razaviyayn, F Farnia, D Tse Advances in Neural Information Processing Systems 28, 2015 | 12 | 2015 |
| Diffpattern: Layout pattern generation via discrete diffusion Z Wang, Y Shen, W Zhao, Y Bai, G Chen, F Farnia, B Yu 2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023 | 11 | 2023 |
| On the role of generalization in transferability of adversarial examples Y Wang, F Farnia Uncertainty in Artificial Intelligence, 2259-2270, 2023 | 10 | 2023 |
| Mode-seeking divergences: theory and applications to GANs CT Li, F Farnia International Conference on Artificial Intelligence and Statistics, 8321-8350, 2023 | 10 | 2023 |
| An information-theoretic evaluation of generative models in learning multi-modal distributions M Jalali, CT Li, F Farnia Advances in Neural Information Processing Systems 36, 9931-9943, 2023 | 9 | 2023 |
| A Wasserstein minimax framework for mixed linear regression T Diamandis, Y Eldar, A Fallah, F Farnia, A Ozdaglar International Conference on Machine Learning, 2697-2706, 2021 | 7 | 2021 |
| On convergence of gradient descent ascent: A tight local analysis H Li, F Farnia, S Das, A Jadbabaie International Conference on Machine Learning, 12717-12740, 2022 | 6 | 2022 |
| A Fourier-based approach to generalization and optimization in deep learning F Farnia, JM Zhang, NT David IEEE Journal on Selected Areas in Information Theory 1 (1), 145-156, 2020 | 6 | 2020 |
| Interpretation of neural networks is susceptible to universal adversarial perturbations HE Oskouie, F Farnia ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 4 | 2023 |
| Moreaugrad: Sparse and robust interpretation of neural networks via moreau envelope J Zhang, F Farnia Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 4 | 2023 |
David TseThomas Kailath and Guanghan Xu Professor, Stanford UniversityVerifierad e-postadress på stanford.edu
