AmirEmad Ghassami

According to our database, AmirEmad Ghassami authored at least 36 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error.

Yuqin Yang, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, AmirEmad Ghassami

CoRR, 2024

Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach.

Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models.

Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash

J. Mach. Learn. Res., 2023

2022

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error.

Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Causal Discovery in Linear Structural Causal Models with Deterministic Relations.

Yuqin Yang, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference.

AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Information Theoretic Measures for Fairness-aware Feature Selection.

Sajad Khodadadian, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash

CoRR, 2021

Multiply Robust Causal Mediation Analysis with Continuous Treatments.

AmirEmad Ghassami, Numair Sani, Yizhen Xu, Ilya Shpitser

CoRR, 2021

Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals.

AmirEmad Ghassami, Andrew Ying, Ilya Shpitser, Eric Tchetgen Tchetgen

CoRR, 2021

Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias.

Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Recursive Markov Boundary-Based Approach to Causal Structure Learning.

Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash

Proceedings of the KDD 2021 Workshop on Causal Discovery, 2021

Impact of Data Processing on Fairness in Supervised Learning.

Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash

Proceedings of the IEEE International Symposium on Information Theory, 2021

2020

Causal discovery beyond Markov equivalence

AmirEmad Ghassami

PhD thesis, 2020

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.

Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang

J. Mach. Learn. Res., 2020

A Recursive Markov Blanket-Based Approach to Causal Structure Learning.

Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash

CoRR, 2020

Model-Augmented Conditional Mutual Information Estimation for Feature Selection.

Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs.

Ignavier Ng, AmirEmad Ghassami, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs.

AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection.

Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum

CoRR, 2019

Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs.

AmirEmad Ghassami, Kun Zhang, Negar Kiyavash

CoRR, 2019

Interventional Experiment Design for Causal Structure Learning.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash

CoRR, 2019

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

A Covert Queueing Channel in FCFS Schedulers.

AmirEmad Ghassami, Negar Kiyavash

IEEE Trans. Inf. Forensics Secur., 2018

REORDER: Securing Dynamic-Priority Real-Time Systems Using Schedule Obfuscation.

Chien-Ying Chen, Monowar Hasan, AmirEmad Ghassami, Sibin Mohan, Negar Kiyavash

CoRR, 2018

Counting and Uniform Sampling from Markov Equivalent DAGs.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash

CoRR, 2018

Multi-domain Causal Structure Learning in Linear Systems.

AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fairness in Supervised Learning: An Information Theoretic Approach.

AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Budgeted Experiment Design for Causal Structure Learning.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

A Covert Queueing Channel in Round Robin Schedulers.

AmirEmad Ghassami, Ali Yekkehkhany, Negar Kiyavash, Yi Lu

CoRR, 2017

Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash

CoRR, 2017

A Reconnaissance Attack Mechanism for Fixed-Priority Real-Time Systems.

Chien-Ying Chen, AmirEmad Ghassami, Sibin Mohan, Negar Kiyavash, Rakesh B. Bobba, Rodolfo Pellizzoni, Man-Ki Yoon

CoRR, 2017

Learning Causal Structures Using Regression Invariance.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Interaction information for causal inference: The case of directed triangle.

AmirEmad Ghassami, Negar Kiyavash

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016

Message partitioning and limited auxiliary randomness: Alternatives to Honey Encryption.

AmirEmad Ghassami, Daniel F. Cullina, Negar Kiyavash

Proceedings of the IEEE International Symposium on Information Theory, 2016

Sneak-Peek: High speed covert channels in data center networks.

Rashid Tahir, Mohammad Taha Khan, Xun Gong, Adnan Ahmed, AmirEmad Ghassami, Hasanat Kazmi, Matthew Caesar, Fareed Zaffar, Negar Kiyavash

Proceedings of the 35th Annual IEEE International Conference on Computer Communications, 2016

2015

Capacity limit of queueing timing channel in shared FCFS schedulers.

AmirEmad Ghassami, Xun Gong, Negar Kiyavash

Proceedings of the IEEE International Symposium on Information Theory, 2015