Interpretable Machine Learning

• H. Rafique, T. Wang, Q. Lin, and A. Singhani. Transparency Promotion with Model-Agnostic Linear Competitors. Proceedings of the Thirty Seven International Conference on Machine Learning (ICML), 2020. 

• H. Rafique, T. Wang, and Q. Lin. Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-box Models, Third INFORMS Workshop on Data Science, INFORMS College on Artificial Intelligence, 2019. Best Paper Award Runner-Up

Optimization

• M. Liu , H. Rafique, Q. Lin, and T. Yang. Provable Non-Convex Min-Max Optimization, Smooth game optimization and machine learning workshop, Neurips 2018 

• M. Liu , H. Rafique, Q. Lin, and T. Yang. First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems . Journal of Machine Learning, 2021. 

• H. Rafique, M. Liu, Q. Lin, and T. Yang. Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning, Optimization Methods and Software, 2021. 

• M. Imran, H. Rafique, A. Khan, and T. Malik. Model of Bi-mode Transmission Dynamics of Hepatitis C with Optimal Control, Theory in Biosciences, 2014.