| Wasserstein adversarial imitation learning H Xiao, M Herman, J Wagner, S Ziesche, J Etesami, TH Linh arXiv preprint arXiv:1906.08113, 2019 | 76 | 2019 |
| Learning network of multivariate hawkes processes: A time series approach J Etesami, N Kiyavash, K Zhang, K Singhal Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence …, 2016 | 75 | 2016 |
| Online learning for multivariate hawkes processes Y Yang, J Etesami, N He, N Kiyavash Advances in Neural Information Processing Systems 30, 2017 | 65 | 2017 |
| Learning minimal latent directed information polytrees J Etesami, N Kiyavash, T Coleman Neural computation 28 (9), 1723-1768, 2016 | 41* | 2016 |
| Directed information graphs: A generalization of linear dynamical graphs J Etesami, N Kiyavash 2014 American control conference, 2563-2568, 2014 | 39 | 2014 |
| LDPC code construction for wireless physical-layer key reconciliation J Etesami, W Henkel 2012 1st IEEE International Conference on Communications in China (ICCC …, 2012 | 35 | 2012 |
| Learning Hawkes processes under synchronization noise W Trouleau, J Etesami, M Grossglauser, N Kiyavash, P Thiran International Conference on Machine Learning, 6325-6334, 2019 | 25 | 2019 |
| Measuring causal relationships in dynamical systems through recovery of functional dependencies J Etesami, N Kiyavash IEEE Transactions on Signal and Information Processing over Networks 3 (4 …, 2016 | 24 | 2016 |
| Causal transfer for imitation learning and decision making under sensor-shift J Etesami, P Geiger Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10118 …, 2020 | 20 | 2020 |
| Sharp analysis of stochastic optimization under global Kurdyka-Lojasiewicz inequality I Fatkhullin, J Etesami, N He, N Kiyavash Advances in Neural Information Processing Systems 35, 15836-15848, 2022 | 16 | 2022 |
| Revisiting the General Identifiability Problem Y Kivva, E Mokhtarian, J Etesami, N Kiyavash 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022 | 12 | 2022 |
| Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity J Etesami, A Habibnia, N Kiyavash Systemic Risk Centre, The London School of Economics and Political Science, 2017 | 12 | 2017 |
| Learning Bayesian Networks in the Presence of Structural Side Information E Mokhtarian, S Akbari, F Jamshidi, J Etesami, N Kiyavash AAAI - Association for the Advancement of Artificial Intelligence 2022, 2021 | 11 | 2021 |
| Optimal attack strategies against predictors-learning from expert advice A Truong, SR Etesami, J Etesami, N Kiyavash IEEE Transactions on Information Forensics and Security 13 (1), 6-19, 2017 | 11 | 2017 |
| Nonparametric hawkes processes: Online estimation and generalization bounds Y Yang, J Etesami, N He, N Kiyavash arXiv preprint arXiv:1801.08273, 2018 | 9 | 2018 |
| Interventional dependency graphs: An approach for discovering influence structure J Etesami, N Kiyavash 2016 IEEE International Symposium on Information Theory (ISIT), 1158-1162, 2016 | 9* | 2016 |
| Causal Effect Identification with Context-specific Independence Relations of Control Variables E Mokhtarian, F Jamshidi, J Etesami, N Kiyavash International Conference on Artificial Intelligence and Statistics (AISTATS …, 2022 | 8 | 2022 |
| Novel ordering-based approaches for causal structure learning in the presence of unobserved variables E Mokhtarian, M Khorasani, J Etesami, N Kiyavash Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 12260 …, 2023 | 6 | 2023 |
| Causal bandits without graph learning M Konobeev, J Etesami, N Kiyavash arXiv preprint arXiv:2301.11401, 2023 | 6 | 2023 |
| A variational inference approach to learning multivariate wold processes J Etesami, W Trouleau, N Kiyavash, M Grossglauser, P Thiran International Conference on Artificial Intelligence and Statistics, 2044-2052, 2021 | 6 | 2021 |
Negar KiyavashÉcole polytechnique fédérale de Lausanne (EPFL)在 epfl.ch 的电子邮件经过验证
