Negar Kiyavash

research

∙ 01/26/2023

Causal Bandits without Graph Learning

We study the causal bandit problem when the causal graph is unknown and ...

0 Mikhail Konobeev, et al. ∙

research

∙ 08/14/2022

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

We propose ordering-based approaches for learning the maximal ancestral ...

0 Ehsan Mokhtarian, et al. ∙

research

∙ 08/09/2022

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

Causal identification is at the core of the causal inference literature,...

0 Sina Akbari, et al. ∙

research

∙ 06/02/2022

Revisiting the General Identifiability Problem

We revisit the problem of general identifiability originally introduced ...

0 Yaroslav Kivva, et al. ∙

research

∙ 05/25/2022

Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions

We study the performance of Stochastic Cubic Regularized Newton (SCRN) o...

0 Saeed Masiha, et al. ∙

research

∙ 05/20/2022

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

We study experiment design for the unique identification of the causal g...

0 Ehsan Mokhtarian, et al. ∙

research

∙ 05/17/2022

Adaptive Momentum-Based Policy Gradient with Second-Order Information

The variance reduced gradient estimators for policy gradient methods has...

0 Saber Salehkaleybar, et al. ∙

research

∙ 05/04/2022

Minimum Cost Intervention Design for Causal Effect Identification

Pearl's do calculus is a complete axiomatic approach to learn the identi...

0 Sina Akbari, et al. ∙

research

∙ 12/20/2021

Learning Bayesian Networks in the Presence of Structural Side Information

We study the problem of learning a Bayesian network (BN) of a set of var...

0 Ehsan Mokhtarian, et al. ∙

research

∙ 10/30/2021

Causal Discovery in Linear Structural Causal Models with Deterministic Relations

Linear structural causal models (SCMs) – in which each observed variable...

0 Yuqin Yang, et al. ∙

research

∙ 10/22/2021

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

We study the problem of causal effect identification from observational ...

0 Ehsan Mokhtarian, et al. ∙

research

∙ 10/22/2021

Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias

We consider the problem of learning the causal MAG of a system from obse...

0 Sina Akbari, et al. ∙

research

∙ 06/01/2021

Information Theoretic Measures for Fairness-aware Feature Selection

Machine learning algorithms are increasingly used for consequential deci...

0 Sajad Khodadadian, et al. ∙

research

∙ 03/29/2021

The Complexity of Nonconvex-Strongly-Concave Minimax Optimization

This paper studies the complexity for finding approximate stationary poi...

0 Siqi Zhang, et al. ∙

research

∙ 02/03/2021

Impact of Data Processing on Fairness in Supervised Learning

We study the impact of pre and post processing for reducing discriminati...

0 Sajad Khodadadian, et al. ∙

research

∙ 10/10/2020

A Recursive Markov Blanket-Based Approach to Causal Structure Learning

One of the main approaches for causal structure learning is constraint-b...

0 Ehsan Mokhtarian, et al. ∙

research

∙ 06/17/2020

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

The causal relationships among a set of random variables are commonly re...

0 Ali AhmadiTeshnizi, et al. ∙

research

∙ 02/22/2020

Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems

Nonconvex minimax problems appear frequently in emerging machine learnin...

0 Junchi Yang, et al. ∙

research

∙ 01/02/2020

Adversarial Policies in Learning Systems with Malicious Experts

We consider a learning system based on the conventional multiplicative w...

0 S. Rasoul Etesami, et al. ∙

research

∙ 11/12/2019

Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection

Markov blanket feature selection, while theoretically optimal, generally...

0 Alan Yang, et al. ∙

research

∙ 10/28/2019

Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs

The main way for defining equivalence among acyclic directed graphs is b...

0 AmirEmad Ghassami, et al. ∙

research

∙ 10/12/2019

Interventional Experiment Design for Causal Structure Learning

It is known that from purely observational data, a causal DAG is identif...

0 AmirEmad Ghassami, et al. ∙

research

∙ 08/11/2019

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

We consider the problem of learning causal models from observational dat...

0 Saber Salehkaleybar, et al. ∙

research

∙ 03/04/2019

Database Alignment with Gaussian Features

We consider the problem of aligning a pair of databases with jointly Gau...

0 Osman Emre Dai, et al. ∙

research

∙ 09/10/2018

Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment

We determine information theoretic conditions under which it is possible...

0 Daniel Cullina, et al. ∙

research

∙ 06/05/2018

ScheduLeak: A Novel Scheduler Side-Channel Attack Against Real-Time Autonomous Control Systems

Real-time autonomous control systems are often the core of safety critic...

0 Chien-Ying Chen, et al. ∙

research

∙ 06/04/2018

REORDER: Securing Dynamic-Priority Real-Time Systems Using Schedule Obfuscation

Modern real-time systems (RTS) are increasingly the focus of security th...

0 Chien-Ying Chen, et al. ∙

research

∙ 05/10/2018

Fundamental Limits of Database Alignment

We consider the problem of aligning a pair of databases with correlated ...

0 Daniel Cullina, et al. ∙

research

∙ 04/25/2018

On the Performance of a Canonical Labeling for Matching Correlated Erdős-Rényi Graphs

Graph matching in two correlated random graphs refers to the task of ide...

0 Osman Emre Dai, et al. ∙

research

∙ 02/05/2018

Counting and Uniform Sampling from Markov Equivalent DAGs

We propose an exact solution for the problem of finding the size of a Ma...

0 AmirEmad Ghassami, et al. ∙

research

∙ 01/25/2018

Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds

In this paper, we design a nonparametric online algorithm for estimating...

0 Yingxiang Yang, et al. ∙

research

∙ 01/13/2018

Fairness in Supervised Learning: An Information Theoretic Approach

Automated decision making systems are increasingly being used in real-wo...

0 AmirEmad Ghassami, et al. ∙

research

∙ 09/11/2017

Budgeted Experiment Design for Causal Structure Learning

We study the problem of causal structure learning when the experimenter ...

0 AmirEmad Ghassami, et al. ∙

research

∙ 05/26/2017

Learning Causal Structures Using Regression Invariance

We study causal inference in a multi-environment setting, in which the f...

0 AmirEmad Ghassami, et al. ∙

research

∙ 05/07/2017

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

In real-time embedded systems (RTS), failures due to security breaches c...

0 Chien-Ying Chen, et al. ∙

research

∙ 03/31/2017

A New Measure of Conditional Dependence

Measuring conditional dependencies among the variables of a network is o...

0 Jalal Etesami, et al. ∙

research

∙ 02/27/2017

Learning Vector Autoregressive Models with Latent Processes

We study the problem of learning the support of transition matrix betwee...

0 Saber Salehkaleybar, et al. ∙

research

∙ 02/27/2017

Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments

We study the problem of causal structure learning over a set of random v...

0 AmirEmad Ghassami, et al. ∙

research

∙ 01/30/2017

Interaction Information for Causal Inference: The Case of Directed Triangle

Interaction information is one of the multivariate generalizations of mu...

0 AmirEmad Ghassami, et al. ∙

research

∙ 03/14/2016

Learning Network of Multivariate Hawkes Processes: A Time Series Approach

Learning the influence structure of multiple time series data is of grea...

0 Jalal Etesami, et al. ∙

research

∙ 09/22/2015

Efficient Neighborhood Selection for Gaussian Graphical Models

This paper addresses the problem of neighborhood selection for Gaussian ...

0 Yingxiang Yang, et al. ∙

research

∙ 04/09/2012

Directed Information Graphs

We propose a graphical model for representing networks of stochastic pro...

0 Christopher J. Quinn, et al. ∙

Negar Kiyavash

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