The Machine Learning Center at Georgia Tech invites you to a seminar by Zhangyang Wang an assistant professor from Texas A&M University.
Title: Learning Augmented Optimization: Faster, Better and Guaranteed
Learning and optimization are closely related: state-of-the-art learning problems hinge on the sophisticated design of optimizers. On the other hand, the optimization cannot be considered as independent from data, since data may implicitly contain important information that guides optimization, as seen in the recent waves of meta-learning or learning to optimize. This talk will discuss Learning Augmented Optimization (LAO), a nascent area that bridges classical optimization with the latest data-driven learning, by augmenting classical model-based optimization with learning-based components. By adapting their behavior to the properties of the input distribution, the ``augmented'' algorithms may reduce their complexities by magnitudes, and/or improve their accuracy, while still preserving favorable theoretical guarantees such as convergence. I will start by diving into a case study on exploiting deep learning to solve the convex LASSO problem, showing its linear convergence in addition to superior parameter efficiency. Then, our discussions will be extended to applying LAO approaches to solving plug-and-play (PnP) optimization, and population-based optimization. I will next demonstrate our recent results on ensuring the robustness of LAO, say how applicable the algorithm remains to be, if the testing problem instances deviate from the training problem distribution. The talk will be concluded by a few thoughts and reflections, as well as pointers to potential future directions.
Dr. Zhangyang (Atlas) Wang is an Assistant Professor of Computer Science and Engineering at Texas A&M University, since 2017. During 2012-2016, he was a Ph.D. student in the Electrical and Computer Engineering (ECE) Department, at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Thomas S. Huang. Dr. Wang is broadly interested in the fields of machine learning, computer vision, optimization, and their interdisciplinary applications. His latest interests focus on addressing automated machine learning (AutoML), learning-based optimization, and efficient deep learning. He has published over 90 papers (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, etc.), 2 books and 1 chapter; has been granted 3 patents, and has received over 20 research awards and scholarships. His research has been gratefully supported by NSF, DARPA, ARL, as well as a number of industry and university grants. More information can be found at https://www.atlaswang.com/