| 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 | 62 | 2020 |
| Budgeted experiment design for causal structure learning AE Ghassami, S Salehkaleybar, N Kiyavash, E Bareinboim International Conference on Machine Learning, 1724-1733, 2018 | 54 | 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 | 44 | 2018 |
| Learning causal structures using regression invariance AE Ghassami, S Salehkaleybar, N Kiyavash, K Zhang Advances in Neural Information Processing Systems 30, 2017 | 44 | 2017 |
| Multi-domain causal structure learning in linear systems AE Ghassami, N Kiyavash, B Huang, K Zhang Advances in neural information processing systems 31, 2018 | 31 | 2018 |
| 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 | 27* | 2016 |
| 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 | 26 | 2016 |
| Learning linear non-Gaussian causal models in the presence of latent variables S Salehkaleybar, AE Ghassami, N Kiyavash, K Zhang The Journal of Machine Learning Research 21 (1), 1436-1459, 2020 | 20 | 2020 |
| 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 | 20 | 2019 |
| 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 | 18 | 2015 |
| 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 | 16* | 2022 |
| 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 | 13 | 2020 |
| 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 | 12 | 2017 |
| A covert queueing channel in FCFS schedulers AE Ghassami, N Kiyavash IEEE Transactions on Information Forensics and Security 13 (6), 1551-1563, 2018 | 11 | 2018 |
| A reconnaissance attack mechanism for fixed-priority real-time systems CY Chen, AE Ghassami, S Mohan, N Kiyavash, RB Bobba, R Pellizzoni, ... arXiv preprint arXiv:1705.02561, 2017 | 10 | 2017 |
| Reorder: Securing dynamic-priority real-time systems using schedule obfuscation CY Chen, M Hasan, AE Ghassami, S Mohan, N Kiyavash arXiv preprint arXiv:1806.01393, 2018 | 9 | 2018 |
| Interventional experiment design for causal structure learning AE Ghassami, S Salehkaleybar, N Kiyavash arXiv preprint arXiv:1910.05651, 2019 | 8 | 2019 |
| 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 | 7 | 2021 |
| Optimal experiment design for causal discovery from fixed number of experiments AE Ghassami, S Salehkaleybar, N Kiyavash arXiv preprint arXiv:1702.08567, 2017 | 6 | 2017 |
| A covert queueing channel in round robin schedulers AE Ghassami, A Yekkehkhany, N Kiyavash arXiv preprint arXiv:1701.08883, 2017 | 6 | 2017 |
Negar KiyavashÉcole polytechnique fédérale de Lausanne (EPFL)Verified email at epfl.ch
