Labeled data is critical to the success of deep learning across various applications, including natural language processing, computer vision, and computational biology. While recent advances like pre-training have reduced the need for labeled data in these domains, increasing the availability of labeled data remains the most effective way to improve model performance. However, human labeling of data continues to be expensive, even when leveraging cost-effective crowd-sourced labeling services. Further, in many domains, labeling requires specialized expertise, which adds to the difficulty of acquiring labeled data.
In this talk, we demonstrate how to utilize weak supervision together with efficient computational algorithms to reduce data labeling costs. Specifically, we investigate various forms of weak supervision, including external knowledge bases, auxiliary computational tools, and heuristic rule-based labeling. We showcase the application of weak supervision to both supervised learning and reinforcement learning across various tasks, including natural language understanding, molecular dynamics simulation, and code generation.
Tuo Zhao is an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering and the school of Computational Science and Engineering (By Courtesy) at Georgia Tech.
His research focuses on developing principled methodologies, nonconvex optimization algorithms and practical theories for machine learning (especially deep learning). He is also interested in natural language processing and actively contributing to open source software development for scientific computing.
Tuo Zhao received his Ph.D. degree in Computer Science at Johns Hopkins University in 2016. He was a visiting scholar in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health from 2010 to 2012, and the Department of Operations Research and Financial Engineering at Princeton University from 2014 to 2016.
He was the core member of the JHU team winning the INDI ADHD 200 global competition on fMRI imaging-based diagnosis classification in 2011. He received the Google summer of code awards from 2011 to 2014. He received the Siebel scholarship in 2014, the Baidu Fellowship in 2015-2016 and Google Faculty Research Award in 2020. He was the co-recipient of the 2016 ASA Best Student Paper Award on Statistical Computing and the 2016 INFORMS SAS Best Paper Award on Data Mining.