Hassan Rafique

University of Indianapolis

H-index: 4

North America-United States

About Hassan Rafique

Hassan Rafique, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at University of Indianapolis, specializes in the field of Optimization, Machine Learning, Sports Analytics.

His recent articles reflect a diverse array of research interests and contributions to the field:

cricWAR: A reproducible system for evaluating player performance in limited-overs cricket

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems

Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning

Provable Methods for Non-Convex Min-Max Optimization and Models for Interpretable Machine Learning

Transparency Promotion with Model-Agnostic Linear Competitors

Hassan Rafique Information

University	University of Indianapolis
Position	___
Citations(all)	364
Citations(since 2020)	354
Cited By	150
hIndex(all)	4
hIndex(since 2020)	4
i10Index(all)	3
i10Index(since 2020)	2
Email	Access Email
University Profile Page	University of Indianapolis
Google Scholar	View Google Scholar Profile

Hassan Rafique Skills & Research Interests

Optimization

Machine Learning

Sports Analytics

Top articles of Hassan Rafique

Title	Journal	Author(s)	Publication Date
cricWAR: A reproducible system for evaluating player performance in limited-overs cricket		Hassan Rafique	2022/10
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems	Journal of Machine Learning Research	Mingrui Liu Hassan Rafique Qihang Lin Tianbao Yang	2021
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning	Optimization Methods and Software	Hassan Rafique Mingrui Liu Qihang Lin Tianbao Yang	2022/5/4
Provable Methods for Non-Convex Min-Max Optimization and Models for Interpretable Machine Learning		Hassan Rafique	2020
Transparency Promotion with Model-Agnostic Linear Competitors		Hassan Rafique Tong Wang Qihang Lin Arshia Singhani	2020/11/21