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20122026
most citedLearning Causal Structures Using Regression Invariance

13 citations · 41 across the 25 of their papers we have counts for

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Showing 2017Show all

7 papers · 1 filter

cs.LG2017

Budgeted Experiment Design for Causal Structure Learning

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash +1

We study the problem of causal structure learning when the experimenter is limited to perform at most non-adaptive experiments of size . We formulate the problem of finding…

cs.IT2017

A Covert Queueing Channel in FCFS Schedulers

AmirEmad Ghassami, Negar Kiyavash

We study covert queueing channels (CQCs), which are a kind of covert timing channel that may be exploited in shared queues across supposedly isolated users. In our system model, a…

cs.LG2017★ 13 cited

Learning Causal Structures Using Regression Invariance

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash +1

We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments…

cs.CR2017

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

Chien-Ying Chen, AmirEmad Ghassami, Sibin Mohan +4

In real-time embedded systems (RTS), failures due to security breaches can cause serious damage to the system, the environment and/or injury to humans. Therefore, it is very import…

cs.LG2017★ 2 cited

Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash

We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most experiments in a non-adaptive manner. We co…

cs.AI2017★ 2 cited

Interaction Information for Causal Inference: The Case of Directed Triangle

AmirEmad Ghassami, Negar Kiyavash

Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount information shared among a set of variables, beyond the informa…