Publications by Seyed Jalal Etesami

On Identifiability of Conditional Causal Effects

Negar Kiyavash, Yaroslav Kivva, Seyed Jalal Etesami

We address the problem of identifiability of an arbitrary conditional causal effect given both the causal graph and a set of any observational and/or interventional distributions of the form Q[S] :" P p(S|do(V/S)), where V denotes the set of all observed v ...

2023

Learning Bayesian Networks in the Presence of Structural Side Information

Negar Kiyavash, Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Seyed Jalal Etesami

We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and statistically challengin ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Revisiting the General Identifiability Problem

Negar Kiyavash, Ehsan Mokhtarian, Yaroslav Kivva, Seyed Jalal Etesami

We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show t ...

2022

Minimum Cost Intervention Design for Causal Effect Identification

Negar Kiyavash, Sina Akbari, Seyed Jalal Etesami

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...

PMLR2022

A Wasserstein-based measure of conditional dependence

Negar Kiyavash, Seyed Jalal Etesami, Kun Zhang

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group ...

2022

Causal Effect Identification with Context-specific Independence Relations of Control Variables

Negar Kiyavash, Ehsan Mokhtarian, Fateme Jamshidi, Seyed Jalal Etesami

We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Optimal Adversarial Policies in the Multiplicative Learning System With a Malicious Expert

Negar Kiyavash, Seyed Jalal Etesami

We consider a learning system based on the conventional multiplicative weight ( MW) rule that combines experts' advice to predict a sequence of true outcomes. It is assumed that one of the experts is malicious and aims to impose the maximum loss on the sys ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Cumulants of Hawkes Processes are Robust to Observation Noise

Patrick Thiran, Matthias Grossglauser, Negar Kiyavash, Seyed Jalal Etesami, William Trouleau

Multivariate Hawkes processes (MHPs) are widely used in a variety of fields to model the occurrence of causally related discrete events in continuous time. Most state-of-the-art approaches address the problem of learning MHPs from perfect traces without no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

A Variational Inference Approach to Learning Multivariate Wold Processes

Patrick Thiran, Matthias Grossglauser, Negar Kiyavash, Seyed Jalal Etesami, William Trouleau

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

MICROTOME PUBLISHING2021

Learning Hawkes Processes Under Synchronization Noise

Patrick Thiran, Matthias Grossglauser, William Trouleau, Negar Kiyavash, Seyed Jalal Etesami

Multivariate Hawkes processes (MHP) are widely used in a variety of fields to model the occurrence of discrete events. Prior work on learning MHPs has only focused on inference in the presence of perfect traces without noise. We address the problem of lear ...

PMLR2019