| On the role of sparsity and dag constraints for learning linear dags I Ng, AE Ghassami, K Zhang Advances in Neural Information Processing Systems 33, 17943-17954, 2020 | 287 | 2020 |
| Learning causal structures using regression invariance AE Ghassami, S Salehkaleybar, N Kiyavash, K Zhang Advances in Neural Information Processing Systems 30, 2017 | 92 | 2017 |
| Budgeted experiment design for causal structure learning AE Ghassami, S Salehkaleybar, N Kiyavash, E Bareinboim International Conference on Machine Learning, 1724-1733, 2018 | 90 | 2018 |
| Multi-domain causal structure learning in linear systems AE Ghassami, N Kiyavash, B Huang, K Zhang Advances in neural information processing systems 31, 2018 | 82 | 2018 |
| Fairness in supervised learning: An information theoretic approach AE Ghassami, S Khodadadian, N Kiyavash 2018 IEEE international symposium on information theory (ISIT), 176-180, 2018 | 77 | 2018 |
| Minimax kernel machine learning for a class of doubly robust functionals with application to proximal causal inference AE Ghassami, A Ying, I Shpitser, ET Tchetgen International conference on artificial intelligence and statistics, 7210-7239, 2022 | 71* | 2022 |
| Learning linear non-Gaussian causal models in the presence of latent variables S Salehkaleybar, AE Ghassami, N Kiyavash, K Zhang Journal of Machine Learning Research 21 (39), 1-24, 2020 | 68 | 2020 |
| Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs AE Ghassami, A Yang, N Kiyavash, K Zhang 37th International Conference on Machine Learning (ICML), 2020 | 41 | 2020 |
| Combining experimental and observational data for identification and estimation of long-term causal effects AE Ghassami, A Yang, D Richardson, I Shpitser, ET Tchetgen arXiv preprint arXiv:2201.10743, 2022 | 37 | 2022 |
| Recursive causal structure learning in the presence of latent variables and selection bias S Akbari, E Mokhtarian, AE Ghassami, N Kiyavash Advances in Neural Information Processing Systems 34, 10119-10130, 2021 | 37 | 2021 |
| Sneak-peek: High speed covert channels in data center networks R Tahir, MT Khan, X Gong, A Ahmed, AE Ghassami, H Kazmi, M Caesar, ... INFOCOM 2016-The 35th Annual IEEE International Conference on Computer …, 2016 | 34 | 2016 |
| Counting and sampling from Markov equivalent DAGs using clique trees AE Ghassami, S Salehkaleybar, N Kiyavash, K Zhang Proceedings of the AAAI conference on artificial intelligence 33 (01), 3664-3671, 2019 | 31 | 2019 |
| ScheduLeak: An Algorithm for Reconstructing Task Schedules in Fixed-Priority Hard Real-Time Systems CY Chen, AE Ghassami, S Mohan, N Kiyavash, RB Bobba, R Pellizzoni Proceedings of the IEEE Workshop on Security and Dependability of Critical …, 2016 | 29* | 2016 |
| Causal inference with hidden mediators A Ghassami, A Yang, I Shpitser, E Tchetgen Tchetgen Biometrika 112 (1), asae037, 2025 | 28* | 2025 |
| A recursive Markov boundary-based approach to causal structure learning E Mokhtarian, S Akbari, AE Ghassami, N Kiyavash The KDD'21 Workshop on Causal Discovery, 26-54, 2021 | 27* | 2021 |
| Interaction information for causal inference: The case of directed triangle AE Ghassami, N Kiyavash 2017 IEEE International Symposium on Information Theory (ISIT), 1326-1330, 2017 | 25 | 2017 |
| Capacity limit of queueing timing channel in shared FCFS schedulers AE Ghassami, X Gong, N Kiyavash 2015 IEEE International Symposium on Information Theory (ISIT), 789-793, 2015 | 19 | 2015 |
| Causal discovery in linear latent variable models subject to measurement error Y Yang, AE Ghassami, M Nafea, N Kiyavash, K Zhang, I Shpitser Advances in Neural Information Processing Systems 35, 874-886, 2022 | 17 | 2022 |
| Interventional experiment design for causal structure learning AE Ghassami, S Salehkaleybar, N Kiyavash arXiv preprint arXiv:1910.05651, 2019 | 15 | 2019 |
| A covert queueing channel in FCFS schedulers AE Ghassami, N Kiyavash IEEE Transactions on Information Forensics and Security 13 (6), 1551-1563, 2018 | 15 | 2018 |
Negar KiyavashÉcole polytechnique fédérale de Lausanne (EPFL)Verified email at epfl.ch
