Negar Kiyavash

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Related publications (31)

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Learning Bayesian Networks in the Presence of Structural Side Information

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

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

Learning Hawkes Processes Under Synchronization Noise

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

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

Causal imitability under context-specific independence relations

Negar Kiyavash, Sina Akbari, Fateme Jamshidi

Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and to circumvent causal confounding and causal misspecifications have been propo ...

Curran Associates, Inc.2023

Momentum-Based Policy Gradient with Second-Order Information

Mohammadsadegh Khorasani, Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

2022

LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments

Negar Kiyavash

The causal relationships among a set of random variables are commonly represented by a Directed Acyclic Graph (DAG), where there is a directed edge from variable X to variable Y if X is a direct cause of Y. From the purely observational data, the true caus ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

A Wasserstein-based measure of conditional dependence

Negar Kiyavash, Seyed Jalal Etesami

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

The KDD 2021 Workshop on Causal Discovery (CD2021)

Negar Kiyavash

As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled exp ...

ASSOC COMPUTING MACHINERY2021

Model-Augmented Conditional Mutual Information Estimation for Feature Selection

Negar Kiyavash

Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independence (CI) testing, which tend to struggle either with the curse of dimension ...

2020

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

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

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

Editorial

Negar Kiyavash

ASSOC COMPUTING MACHINERY2021

Special Issue on Causality: Fundamental Limits and Applications

Negar Kiyavash

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023