Qiang Wu

Director, MS and Graduate Certificate in Data Science

Professor in Mathematics and Actuarial Science

Faculty of the Computational and Data Science PhD Program

Associate of the Society of Actuaries (ASA)

Office: KOM 322A

Phone: (615) 898 -2053

Email: qwu at mtsu dot edu

Research Interests

Computational Learning Theory, Machine Learning, Big Data, Deep Learning

Funding Support

• Co-PI, USDA REEU, 2023-2028
• PI, NSF DMS-2110826, 7/1/2021-6/30/2024.
• PI, Simons Foundation Collaboration Grants for Mathematicians 712916, 9/1/2020-8/31/2025.
• Co-PI, USDA NLGCA Program, 12/1/2015-11/30/2018.
• Co-PI, TBR Research Grant, 2014-2015.

Selected Publications

• Hongwei Sun and Qiang Wu. Optimal rates of distributed regression with imperfect kernels. Journal of Machine Learning Research, 22(171):1-34, 2021. [journal link]
• X. Guo, T. Hu and Q. Wu, Distributed Minimum Error Entropy Algorithms Journal of Machine Learning Research, 21(126):1-31, 2020. [journal link]
• Y. Feng and Q. Wu, Learning under (1+ε)-moment conditions, Applied and Computational Harmonic Analysis, 49 (2020), 229-256. [journal link]
• T. Hu, Q. Wu, and D.-X. Zhou. Distributed kernel gradient descent algorithm for minimum error entropy principle. Applied and Computational Harmonic Analysis, 49:1 (2020), 229-256. [journal link]
• Z.-C. Guo, L. Shi, and Q. Wu. Learning theory of distributed regression with bias corrected regularization kernel network. Journal of Machine Learning Research, 18:118 (2017), 1-25. [journal link]
• J. Fan, T. Hu, Q. Wu, and D.-X. Zhou. Consistency analysis of minimum error entropy algorithm. Applied and Computational Harmonic Analysis, 41(2016), 164-189. [journal link]
• T. Hu, J. Fan, Q. Wu, and D.-X. Zhou. Learning theory approach to minimum error entropy criterion. Journal of Machine Learning Research, 14(2013), 377-397. [journal link]
• H. Sun and Q. Wu. Least square regression with indefinite kernels and coefficient regularization. Applied and Computational Harmonic Analysis, 30:1(2011), 96-109. [journal link]
• J. Guinney, Q. Wu, and S. Mukherjee. Estimating variable structure and dependence in multi-task learning via gradients. Machine Learning, 83:3(2011), 265-287. [journal link]
• Q. Wu, J. Guinney, M. Maggioni, and S. Mukherjee. Learning gradients: predictive models that infer geometry and dependence, Journal of Machine Learning Research, 11(2010), 2175-2198. [journal link ]
• Q. Wu, F. Liang, and S. Mukherjee. Localized sliced inverse regression. NIPS 21(2009), 1785-1792. [aricle link]
• H. Sun and Q. Wu. A note on application of integral operator in learning theory. Applied and Computational Harmonic Analysis, 26(2009), 416-421. [journal link]
• N. Phalai, Q. Wu, F. Liang, S. Mukherjee, and R. L. Wolpert. Characterizing the function space for Bayesian kernel models. Journal of Machine Learning Research, 8(2007), 1769-1797. [journal link]
• S. Mukherjee and Q. Wu. Estimation of gradients and coordinate covariation in classification. Journal of Machine Learning Research, 7(2006), 2481-2514. [journal link]
• Q. Wu, Y. Ying, and D.-X. Zhou. Learning rates of least square regularized regression. Foundations of Computational Mathematics, 6:2(2006), 171-192. [journal link]
• D.-R. Chen, Q. Wu, Y. Ying, and D.-X. Zhou. Support vector machine soft margin classifiers: error analysis. Journal of Machine Learning Research, 5(2004), 1143-1175. [journal link]

Full publication list see here.