讲座信息
主题:从数据中学习优化算法
Learning Optimization Algorithms from Data
嘉宾:汪张扬 Atlas Wang
地点:腾讯会议
时间:2021/12/18(周六)21:00
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讲座摘要
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 to Optimize (L2O), 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. Our discussions will cover basic L2O methods and categories, their applications to practical problems, as well as thoughts and reflections on future directions.
个人简介
Professor Zhangyang “Atlas” Wang is currently the Jack Kilby/Texas Instruments Endowed Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin. He also holds a visiting researcher position at Amazon. He was an Assistant Professor of Computer Science and Engineering, at the Texas A&M University, from 2017 to 2020. He received his Ph.D. degree in ECE from UIUC in 2016, advised by Professor Thomas S. Huang; and his B.E. degree in EEIS from USTC in 2012. Prof. Wang has broad research interests in machine learning, computer vision, optimization, and their interdisciplinary applications. His research group (https://vita-group.github.io/) recently studies automated machine learning (AutoML), learning to optimize (L2O), robust learning, efficient learning, and graph neural networks.
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