Articles avec mandats d'accès public - AmirEmad GhassamiEn savoir plus
Non disponibles : 2
Message partitioning and limited auxiliary randomness: Alternatives to Honey Encryption
AE Ghassami, D Cullina, N Kiyavash
2016 IEEE International Symposium on Information Theory (ISIT), 1371-1375, 2016
Exigences : US National Science Foundation
Data-driven Reliability for Datacenter Hard Disk Drives
A Yang, AE Ghassami, E Rosenbaum, N Kiyavash
EDFA Technical Articles 21 (2), 16-20, 2019
Exigences : US National Science Foundation
Disponibles quelque part : 17
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
Exigences : US Department of Defense
Budgeted experiment design for causal structure learning
AE Ghassami, S Salehkaleybar, N Kiyavash, E Bareinboim
International Conference on Machine Learning, 1724-1733, 2018
Exigences : US National Science Foundation
Multi-domain causal structure learning in linear systems
AE Ghassami, N Kiyavash, B Huang, K Zhang
Advances in neural information processing systems 31, 2018
Exigences : US National Science Foundation, US Department of Defense, US National …
Learning causal structures using regression invariance
AE Ghassami, S Salehkaleybar, N Kiyavash, K Zhang
Advances in Neural Information Processing Systems 30, 2017
Exigences : US Department of Defense, US National Institutes of Health
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
Exigences : US National Science Foundation
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
Exigences : US National Institutes of Health
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
Exigences : US National Science Foundation, US Department of Defense, US National …
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
Exigences : US National Science Foundation, US Department of Defense, US National …
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
Exigences : US Department of Defense
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
Exigences : US Department of Defense
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
Exigences : US Department of Defense
A covert queueing channel in FCFS schedulers
AE Ghassami, N Kiyavash
IEEE Transactions on Information Forensics and Security 13 (6), 1551-1563, 2018
Exigences : US National Science Foundation
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
Exigences : US National Science Foundation, Fonds national suisse, US Department of …
Impact of data processing on fairness in supervised learning
S Khodadadian, AE Ghassami, N Kiyavash
2021 IEEE International Symposium on Information Theory (ISIT), 2643-2648, 2021
Exigences : US National Science Foundation
Causal discovery in linear structural causal models with deterministic relations
Y Yang, MS Nafea, AE Ghassami, N Kiyavash
Conference on Causal Learning and Reasoning, 944-993, 2022
Exigences : Fonds national suisse
A unified experiment design approach for cyclic and acyclic causal models
E Mokhtarian, S Salehkaleybar, AE Ghassami, N Kiyavash
Journal of Machine Learning Research 24 (354), 1-31, 2023
Exigences : Fonds national suisse
Model-Augmented Conditional Mutual Information Estimation for Feature Selection
A Yang, AE Ghassami, M Raginsky, N Kiyavash, E Rosenbaum
36th Conference on Uncertainty in Artificial Intelligence (UAI) 124, 2020
Exigences : US National Science Foundation, US Department of Defense
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