AI Thrust Seminar | Autonomous Learning from Knowledge Graph
Supporting the below United Nations Sustainable Development Goals:支持以下聯合國可持續發展目標：支持以下联合国可持续发展目标：
Embracing human knowledge into the learning process is of great importance and interests to understand the consequence of embracing deep learning’s Cornucopla. In this talk, I will present our recent attempts on developing techniques for autonomous learning from knowledge graphs, which target to simultaneously learn with and discover knowledge from graph-structured data, like knowledge graphs. Specifically, I will introduce our methods on make hyper-parameter tuning efficient, model search automated and learned subgraph structure interpretable. Our methods have been published in, e.g., Pattern (Cell), TPAMI, NeurIPS, ACL. Besides, our methods are also the best solution on knowledge graph tasks of open-graph-benchmarks.
Dr. Quanming Yao currently is a tenure-track assistant professor at Department of Electronic Engineering, Tsinghua University. Before that, he spent three years from a researcher to a senior scientist in 4Paradigm INC, where he set up and led the company's machine learning research team. He obtained his Ph.D. degree at the Department of Computer Science and Engineering of Hong Kong University of Science and Technology (HKUST). He has published 60 top-tier papers with about 5000 citations and h-index 27. He regularly serves as area chairs for ICML, NeurIPS and ICLR and is associate editors of Neural Network and Machine Learning journal. He is also a receipt of National Youth Talent Plan (China), Forbes 30 Under 30 (China), Young Scientist Awards (Hong Kong Institution of Science), and Google Fellowship (in machine learning).