Title：Meta-learning for Optimization
Speaker：Yutian Chen
Staff Research Scientist
Deepmind
Host：Professor Yisen Wang
Date & Time：2023/8/15 10:00 - 11:30
VooV Meeting：563 818 373
Abstract:
The fact that our world is fundamentally interconnected presents unique challenges for modern data-driven research. In this talk, I will present my research on investigating the interconnected world through the lens of graphs. Specifically, I will demonstrate my pioneering research in deep graph generative models which can generate novel realistic graph structures toward desirable objectives. This line of work has broad applications in molecule design and drug discovery. Next, I will cover my research in representing neural networks as relational graphs, which advances the design and understanding of deep neural networks and connects to network science and neuroscience. Lastly, I will briefly discuss exciting future directions related to graphs and LLMs. Overall, the talk will outline the promising path toward bridging interdisciplinary research and extending the frontiers of AI with graphs.
Biography:
Dr Yutian Chen is a staff research scientist at DeepMind. He obtained the B.E. of Electronic Engineering from Tsinghua University, and PhD in machine learning at the University of California, Irvine, and later worked at the University of Cambridge as a research associate (Postdoc) before joining DeepMind. Yutian took part in the AlphaGo and AlphaGo Zero project, developed Game Go AI programs that defeated the world champions. The AlphaGo project was ranked in the top 10 discoveries of the decade 2010s by the New Scientist magazine. Yutian has conducted research in multiple machine learning areas including Bayesian methods, offline reinforcement learning, generative models and meta-learning with applications in gaming AI, computer vision and text-to-speech. Yutian also serves as area chairs for multiple academic conferences including AISTATS, ICLR, NeurIPS and ICML.
