A Cross-Moment Approach for Causal Effect Estimation

Y. Kivva; S. Salehkaleybar; N. Kiyavash 

Causal Effect Identification in Uncertain Causal Networks

S. Akbari; F. Jamshidi; E. Mokhtarian; M. J. Vowels; J. Etesami et al. 

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

E. Mokhtarian; S. Salehkaleybar; A. Ghassami; N. Kiyavash 

Causal imitability under context-specific independence relations

F. Jamshidi; S. Akbari; N. Kiyavash 

On Identifiability of Conditional Causal Effects

Y. Kivva; J. Etesami; N. Kiyavash 

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

Momentum-Based Policy Gradient with Second-Order Information

S. Salehkaleybar; M. Khorasani; N. Kiyavash; N. He; P. Thiran 

(Dis)assortative partitions on random regular graphs

F. Behrens; G. Arpino; Y. Kivva; L. Zdeborova 

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

S. Masiha; S. Salehkaleybar; N. He; N. Kiyavash; P. Thiran 

A Wasserstein-based measure of conditional dependence

J. Etesami; K. Zhang; N. Kiyavash 

Minimum Cost Intervention Design for Causal Effect Identification

S. Akbari; J. Etesami; N. Kiyavash 

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

S. Akbari; F. Jamshidi; E. Mokhtarian; M. J. Vowels; J. Etesami et al. 

Revisiting the General Identifiability Problem

Y. Kivva; E. Mokhtarian; J. Etesami; N. Kiyavash 

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

E. Mokhtarian; F. Jamshidi; J. Etesami; N. Kiyavash 

Editorial

A. Gandhi; N. Kiyavash; J. Wang 

Adversarial orthogonal regression: Two non-linear regressions for causal inference

M. R. Heydari; S. Salehkaleybar; K. Zhang 

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

S. R. Etesami; N. Kiyavash; V. Leon; H. V. Poor 

Impact of Data Processing on Fairness in Supervised Learning

S. Khodadadian; A. Ghassami; N. Kiyavash 

The KDD 2021 Workshop on Causal Discovery (CD2021)

Thuc Duy Le; J. Li; G. Cooper; S. Triantafyllou; E. Bareinboim et al. 

Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]

A. G. Marques; N. Kiyavash; J. M. F. Moura; D. van de Ville; R. Willett 

Model-Augmented Conditional Mutual Information Estimation for Feature Selection

A. Yang; A. Ghassami; M. Raginsky; N. Kiyavash; E. Rosenbaum 

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

A. AhmadiTeshnizi; S. Salehkaleybar; N. Kiyavash 

A Catalyst Framework for Minimax Optimization

J. Yang; S. Zhang; N. Kiyavash; N. He 

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs

A. Ghassami; A. Yang; N. Kiyavash; K. Zhang 

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

J. Yang; N. Kiyavash; N. He 

Achievability of nearly-exact alignment for correlated Gaussian databases

O. E. Dai; N. Kiyavash; D. Cullina 

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models

Y. Yang; N. Kiyavash; L. Song; N. He 

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

S. Salehkaleybar; A. Ghassami; N. Kiyavash; K. Zhang 

Learning Positive Functions with Pseudo Mirror Descent

Y. Yang; H. Wang; N. Kiyavash; N. He 

Learning Hawkes Processes Under Synchronization Noise

W. Trouleau; J. Etesami; M. Grossglauser; N. Kiyavash; P. Thiran 

Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs

O. E. Dai; D. Cullina; N. Kiyavash; M. Grossglauser 

Database Alignment with Gaussian Features

O. E. Dai; D. Cullina; N. Kiyavash 

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees

A. E. Ghassami; S. Salehkaleybar; N. Kiyavash; K. Zhang