Title: Learning to Optimize

Abstract: Real-world optimization problems are becoming increasingly complex such that off-the-shelf algorithms could hardly offer satisfactory performance. On the other hand, the prior knowledge and efforts needed for manually designing a new dedicated algorithm may, in many cases, unaffordable. A data-driven paradigm, termed Learn to Optimize (L2O), provide a potentially powerful way for automated algorithm/solver design. This talk provides an overview on L2O, including the motivating background, key research questions, recent progress, as well as successful case studies.

Biography: Ke Tang is a Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech). His major research interests include evolutionary computation and machine learning, as well as their applications. He is a Fellow of IEEE and Changjiang Scholar Professor for Artificial Intelligence. He is also the recipient of a few national and international awards, such as the IEEE Computational Intelligence Society Outstanding Early Career Award, the Natural Science Award of Ministry of Education of China, and the Newton Advanced Fellowship of the Royal Society, UK.