activity
20112026
most citedGlobal Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems

33 citations · 107 across the 22 of their papers we have counts for

collaborators
Showing cs.LGShow all

15 papers · 1 filter

cs.LG2023

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…

cs.LG2023

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…

cs.LG20221 cited

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…

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…

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…