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AmirEmad Ghassami
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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
1422020
Budgeted experiment design for causal structure learning
AE Ghassami, S Salehkaleybar, N Kiyavash, E Bareinboim
International Conference on Machine Learning, 1724-1733, 2018
682018
Learning causal structures using regression invariance
AE Ghassami, S Salehkaleybar, N Kiyavash, K Zhang
Advances in Neural Information Processing Systems 30, 2017
652017
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
592018
Multi-domain causal structure learning in linear systems
AE Ghassami, N Kiyavash, B Huang, K Zhang
Advances in neural information processing systems 31, 2018
562018
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
342020
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
32*2022
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
302016
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
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
262020
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
252019
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
192021
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
192017
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
182015
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
13*2021
A covert queueing channel in FCFS schedulers
AE Ghassami, N Kiyavash
IEEE Transactions on Information Forensics and Security 13 (6), 1551-1563, 2018
132018
Interventional experiment design for causal structure learning
AE Ghassami, S Salehkaleybar, N Kiyavash
arXiv preprint arXiv:1910.05651, 2019
112019
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
112018
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
102022
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
102017
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