Yik-Chung Wu, Professor, University of Hong Kong

Yafeng Liu, Associate Professor

Tunning-free matrix and tensor factorizations for signal processing and machine learning

10:30-11:30 March 25 (Monday), S515

Matrix and Tensor factorizations are important data analytic tools in many applications, such as recommendation systems, image completion, social network data mining, wireless communications, etc. Traditionally, matrix and tensor factorizations are approached from optimization perspective. While proven to be effective, optimization-based matrix and tensor factorizations usually involve hyperparameters tuning, with one of the major hyperparameters being the matrix or tensor rank.  However, when the number of hyperparameters is more than 3 or 4, tuning them becomes computationally expensive. This talk approaches the problem from the Bayesian perspective and shows how hyperparameter tuning can be eliminated while providing comparable or even better performance than corresponding optimization-based algorithms.

报告人简介：Yik-Chung Wu received the B.Eng. (EEE) degree in 1998 and the M.Phil. degree in 2001 from the University of Hong Kong (HKU).  He received the Croucher Foundation scholarship in 2002 to study Ph.D. degree at Texas A&M University, College Station, and graduated in 2005.  From August 2005 to August 2006, he was with the Thomson Corporate Research, Princeton, NJ, as a Member of Technical Staff.  Since September 2006, he has been with HKU, currently as an Associate Professor.  He was a visiting scholar at Princeton University, in summers of 2015 and 2017.

His research interests are in general areas of signal processing and communication systems, and in particular Bayesian inference, distributed algorithms, and large-scale optimization.  Dr. Wu served as an Editor for IEEE Communications Letters, and IEEE Transactions on Communications. He is currently a Senior Area Editor for IEEE Transactions on Signal Processing, an Associate Editor for IEEE Wireless Communications Letters, and an Editor for Journal of Communications and Networks.  He was a symposium chair for many interntaional conferences, including IEEE International Conference on Communicaitons (ICC) 2023.  He received four best paper awards in international conferences, with the most recent one from IEEE International Conference on Communications (ICC) 2020.  He is a senior member of the IEEE.