Prof. Sergios Theodoridis
Learning sparse models has been a topic at the forefront of research for the last ten years or so. Considerable effort has been invested in developing efficient schemes for the recovery of sparse signal/parameter vectors. In the sequel, distributed learning techniques are reviewed with an emphasis on greedy-type batch as well as online versions. The task of robust learning in the presence of outliers is then reviewed and new methods, based on the explicit modeling of the outliers, in the context of sparsity-aware learning, will be presented. The new method, based on greedy-type arguments, enjoys a number of merits, compared to more classical techniques. Furthermore, strong theoretical results have been established, for the first time, in such a type of treatment of the robust estimation task. Finally, dictionary learning, in its very recent online and distributed processing framework, is discussed and new experimental as well as theoretical results will be presented.
Sergios Theodoridis is currently a professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens. His research interests lie in the areas of Adaptive Algorithms, Distributed and Sparsity-Aware Learning, Machine Learning and Pattern Recognition, Signal Processing for Audio Processing and Retrieval. He currently serves as Editor-in-Chief for the IEEE Transactions on Signal Processing. He is an IEEE fellow and has served as an IEEE Distinguished Lecturer for both IEEE Signal Processing Society and the IEEE Circuits and Systems Society.