Publications by Negar Kiyavash

Momentum-Based Policy Gradient with Second-Order Information

Patrick Thiran, Negar Kiyavash, Mohammadsadegh Khorasani, 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

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

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

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

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

Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correc ...

2022

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

The KDD 2021 Workshop on Causal Discovery (CD2021)

Negar Kiyavash, Huan Liu

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

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 ...

PMLR2021

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

Negar Kiyavash

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Momentum-Based Policy Gradient with Second-Order Information

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Revisiting the General Identifiability Problem

Minimum Cost Intervention Design for Causal Effect Identification

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

A Wasserstein-based measure of conditional dependence

The KDD 2021 Workshop on Causal Discovery (CD2021)

A Variational Inference Approach to Learning Multivariate Wold Processes

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

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Revisiting the General Identifiability Problem

Minimum Cost Intervention Design for Causal Effect Identification

A Wasserstein-based measure of conditional dependence

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

The KDD 2021 Workshop on Causal Discovery (CD2021)

A Variational Inference Approach to Learning Multivariate Wold Processes

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

Momentum-Based Policy Gradient with Second-Order Information