Monday, March 02, 2020
ByteDance AI Lab
Interpretable Machine Learning for Information Creation and Sharing
Intelligence and machine learning have been transforming the way people create, access, share, and consume information and identify misinformation. Yet tremendous challenges remain about the effective creation of high-quality and useful content and ensuring
universal access to fair and authentic information. In this talk, I will present our themed efforts on developing interpretable machine learning models to represent complex data and scalable algorithms that utilize this stream of formalisms to automatically
generate text-based information. As a showcase, I will describe a novel algorithm for controllable text generation based on the idea of iterative editing via the Monte-Carlo Markov Chain. It has been successfully deployed for advertisement design and creation
in a large online advertising system, serving a hundred thousand enterprises. The work has helped disadvantageous producers such as farmers in under-developed areas to promote their products effectively with very little cost and has the potential to revolutionize
the advertising industry. Finally, I will outline the remaining challenges and promising future directions to tackle them.
Lei Li is Director and a research scientist of ByteDance AI Lab, leading the research and product development for machine translation, machine writing, and intelligent robot. His research interest is on machine learning, data mining, and natural language processing.
Lei received his Ph.D. in Computer Science from Carnegie Mellon University in 2011. His dissertation work on fast algorithms for mining co-evolving time series was awarded ACM KDD best dissertation (runner up). His recent work on AI writer Xiaomingbot received
the 2nd-class award of WU Wenjun AI prize (the top AI award in China) in 2017. He is a recipient of CCF distinguished speaker in 2017, and CCF Young Elite award in 2019 (only 6 recipients). Before ByteDance, he worked as a postdoctoral researcher in the EECS
department of UC Berkeley and a principal research scientist in Baidu's Institute of Deep Learning in Silicon Valley. He has served as 2017 KDD Cup co-Chair, 2018 KDD hands-on tutorial co-chair, 2019/2020 KDD sponsorship co-chair, Area Chairs/SPCs/PCs for EMNLP,
NAACL, AAAI, IJCAI, ICML, NeurIPS, etc. He has published over 50 papers and holds multiple patents from the US and China.