Capacity limit of queueing timing channel in shared FCFS schedulers. - AmirEmad Ghassami; Xun Gong 001; Negar Kiyavash (ISIT, 2015)

Sneak-Peek: High speed covert channels in data center networks. - Rashid Tahir; Mohammad Taha Khan; Xun Gong 001; Adnan Ahmed; AmirEmad Ghassami; Hasanat Kazmi; Matthew Caesar 001; Fareed Zaffar; Negar Kiyavash (INFOCOM, 2016)

Message partitioning and limited auxiliary randomness: Alternatives to Honey Encryption. - AmirEmad Ghassami; Daniel F. Cullina; Negar Kiyavash (ISIT, 2016)

Interaction information for causal inference: The case of directed triangle. - AmirEmad Ghassami; Negar Kiyavash (ISIT, 2017)

Learning Causal Structures Using Regression Invariance. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash; Kun Zhang 001 (NIPS, 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)

Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash (CoRR, 2017)

A Covert Queueing Channel in Round Robin Schedulers. - AmirEmad Ghassami; Ali Yekkehkhany; Negar Kiyavash; Yi Lu (CoRR, 2017)

Budgeted Experiment Design for Causal Structure Learning. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash; Elias Bareinboim (ICML, 2018)

Fairness in Supervised Learning: An Information Theoretic Approach. - AmirEmad Ghassami; Sajad Khodadadian; Negar Kiyavash (ISIT, 2018)

Multi-domain Causal Structure Learning in Linear Systems. - AmirEmad Ghassami; Negar Kiyavash; Biwei Huang; Kun Zhang 001 (NeurIPS, 2018)

Counting and Uniform Sampling from Markov Equivalent DAGs. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash (CoRR, 2018)

REORDER: Securing Dynamic-Priority Real-Time Systems Using Schedule Obfuscation. - Chien-Ying Chen; Monowar Hasan; AmirEmad Ghassami; Sibin Mohan; Negar Kiyavash (CoRR, 2018)

A Covert Queueing Channel in FCFS Schedulers. - AmirEmad Ghassami; Negar Kiyavash (IEEE Trans. Inf. Forensics Secur., 2018)

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash; Kun Zhang 001 (AAAI, 2019)

Interventional Experiment Design for Causal Structure Learning. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash (CoRR, 2019)

Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs. - AmirEmad Ghassami; Kun Zhang 001; Negar Kiyavash (CoRR, 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 and Structure Learning for Cyclic and Acyclic Directed Graphs. - AmirEmad Ghassami; Alan Yang; Negar Kiyavash; Kun Zhang 001 (ICML, 2020)

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. - Ignavier Ng; AmirEmad Ghassami; Kun Zhang 001 (NeurIPS, 2020)

Model-Augmented Conditional Mutual Information Estimation for Feature Selection. - Alan Yang; AmirEmad Ghassami; Maxim Raginsky; Negar Kiyavash; Elyse Rosenbaum (UAI, 2020)

A Recursive Markov Blanket-Based Approach to Causal Structure Learning. - Ehsan Mokhtarian; Sina Akbari; AmirEmad Ghassami; Negar Kiyavash (CoRR, 2020)

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. - Saber Salehkaleybar; AmirEmad Ghassami; Negar Kiyavash; Kun Zhang 001 (J. Mach. Learn. Res., 2020)

Causal discovery beyond Markov equivalence - AmirEmad Ghassami (2020)

Impact of Data Processing on Fairness in Supervised Learning. - Sajad Khodadadian; AmirEmad Ghassami; Negar Kiyavash (ISIT, 2021)

A Recursive Markov Boundary-Based Approach to Causal Structure Learning. - Ehsan Mokhtarian; Sina Akbari; AmirEmad Ghassami; Negar Kiyavash (CD@KDD, 2021)

Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias. - Sina Akbari; Ehsan Mokhtarian; AmirEmad Ghassami; Negar Kiyavash (NeurIPS, 2021)

Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals. - AmirEmad Ghassami; Andrew Ying; Ilya Shpitser; Eric Tchetgen Tchetgen (CoRR, 2021)

Multiply Robust Causal Mediation Analysis with Continuous Treatments. - AmirEmad Ghassami; Numair Sani; Yizhen Xu; Ilya Shpitser (CoRR, 2021)

Information Theoretic Measures for Fairness-aware Feature Selection. - Sajad Khodadadian; Mohamed S. Nafea; AmirEmad Ghassami; Negar Kiyavash (CoRR, 2021)

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 (AISTATS, 2022)

Causal Discovery in Linear Structural Causal Models with Deterministic Relations. - Yuqin Yang; Mohamed S. Nafea; AmirEmad Ghassami; Negar Kiyavash (CLeaR, 2022)

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. - Yuqin Yang; AmirEmad Ghassami; Mohamed S. Nafea; Negar Kiyavash; Kun Zhang 001; Ilya Shpitser (NeurIPS, 2022)

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models. - Ehsan Mokhtarian; Saber Salehkaleybar; AmirEmad Ghassami; Negar Kiyavash (J. Mach. Learn. Res., 2023)

Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach. - Zixiao Wang 006; AmirEmad Ghassami; Ilya Shpitser (ICML, 2024)

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection. - Jaron Jia Rong Lee; AmirEmad Ghassami; Ilya Shpitser (UAI, 2024)

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error. - Yuqin Yang; Mohamed S. Nafea; Negar Kiyavash; Kun Zhang 001; AmirEmad Ghassami (CoRR, 2024)