50 citations · 383 across the 69 of their papers we have counts for
74 papers
Zeroth-Order Stackelberg Control in Combinatorial Congestion Games
Saeed Masiha, Sepehr Elahi, Negar Kiyavash +1
We study Stackelberg (leader--follower) tuning of network parameters (tolls, capacities, incentives) in combinatorial congestion games, where selfish users choose discrete routes (…
Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework
Jalal Etesami, Ali Habibnia, Negar Kiyavash
We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the…
Efficiently Escaping Saddle Points for Policy Optimization
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +2
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance. In recent years, several variance-reduced PG methods have been proposed w…
Gaussian Database Alignment and Gaussian Planted Matching
Osman Emre Dai, Daniel Cullina, Negar Kiyavash
Database alignment is a variant of the graph alignment problem: Given a pair of anonymized databases containing separate yet correlated features for a set of users, the problem is…
On Identifiability of Conditional Causal Effects
Yaroslav Kivva, Jalal Etesami, Negar Kiyavash
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 o…
Causal Imitability Under Context-Specific Independence Relations
Fateme Jamshidi, Sina Akbari, Negar Kiyavash
Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and t…
A Cross-Moment Approach for Causal Effect Estimation
Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash
We consider the problem of estimating the causal effect of a treatment on an outcome in linear structural causal models (SCM) with latent confounders when we have access to a singl…
Learning Causal Graphs via Monotone Triangular Transport Maps
Sina Akbari, Luca Ganassali, Negar Kiyavash
We study the problem of causal structure learning from data using optimal transport (OT). Specifically, we first provide a constraint-based method which builds upon lower-triangula…
Causal Bandits without Graph Learning
Mikhail Konobeev, Jalal Etesami, Negar Kiyavash
We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions. We…
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea +3
We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms perta…
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Łojasiewicz Inequality
Ilyas Fatkhullin, Jalal Etesami, Niao He +1
We study the complexity of finding the global solution to stochastic nonconvex optimization when the objective function satisfies global Kurdyka-Lojasiewicz (KL) inequality and the…
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami +1
We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of…
Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian +3
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…
Revisiting the General Identifiability Problem
Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami +1
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 obs…
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function
Saeed Masiha, Saber Salehkaleybar, Niao He +2
We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with which holds in a wide range o…
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami +1
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces maj…
Momentum-Based Policy Gradient with Second-Order Information
Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash +2
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 acceleratio…
Experimental Design for Causal Effect Identification
Sina Akbari, Jalal Etesami, Negar Kiyavash
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 p…
Learning Bayesian Networks in the Presence of Structural Side Information
Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi +2
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 st…
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
Yuqin Yang, Mohamed Nafea, AmirEmad Ghassami +1
Linear structural causal models (SCMs) -- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources -- a…
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami +1
We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of t…
Causal Effect Identification with Context-specific Independence Relations of Control Variables
Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami +1
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…
Information Theoretic Measures for Fairness-aware Feature Selection
Sajad Khodadadian, Mohamed Nafea, AmirEmad Ghassami +1
Machine learning algorithms are increasingly used for consequential decision making regarding individuals based on their relevant features. Features that are relevant for accurate…
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization
Siqi Zhang, Junchi Yang, Cristóbal Guzmán +2
This paper studies the complexity for finding approximate stationary points of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth finit…