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20112021
most citedGlobal Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems

33 citations · 99 across the 15 of their papers we have counts for

collaborators

31 papers

cs.LG20212 cited

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…

cs.LG20215 cited

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…

math.OC202110 cited

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…

cs.LG2021

Impact of Data Processing on Fairness in Supervised Learning

Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash

We study the impact of pre and post processing for reducing discrimination in data-driven decision makers. We first analyze the fundamental trade-off between fairness and accuracy…

stat.ML20206 cited

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

Ali AhmadiTeshnizi, Saber Salehkaleybar, 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 to variable $Y…

math.OC202033 cited

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

Junchi Yang, Negar Kiyavash, Niao He

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as…