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AmirEmad Ghassami
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Budgeted experiment design for causal structure learning
AE Ghassami, S Salehkaleybar, N Kiyavash, E Bareinboim
International Conference on Machine Learning, 1724-1733, 2018
482018
Learning causal structures using regression invariance
AE Ghassami, S Salehkaleybar, N Kiyavash, K Zhang
Advances in Neural Information Processing Systems 30, 2017
432017
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
392018
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
372020
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
26*2016
Multi-domain causal structure learning in linear systems
AE Ghassami, N Kiyavash, B Huang, K Zhang
Advances in neural information processing systems 31, 2018
242018
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
242016
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
182019
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
172015
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.
S Salehkaleybar, AE Ghassami, N Kiyavash, K Zhang
J. Mach. Learn. Res. 21, 39:1-39:24, 2020
132020
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
12*2022
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
112017
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
102020
A covert queueing channel in FCFS schedulers
AE Ghassami, N Kiyavash
IEEE Transactions on Information Forensics and Security 13 (6), 1551-1563, 2018
102018
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
Interventional experiment design for causal structure learning
AE Ghassami, S Salehkaleybar, N Kiyavash
arXiv preprint arXiv:1910.05651, 2019
72019
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
62021
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
62018
Optimal experiment design for causal discovery from fixed number of experiments
AE Ghassami, S Salehkaleybar, N Kiyavash
arXiv preprint arXiv:1702.08567, 2017
52017
A covert queueing channel in round robin schedulers
AE Ghassami, A Yekkehkhany, N Kiyavash
arXiv preprint arXiv:1701.08883, 2017
52017
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