Data Summarization for Machine Learning
COM1 Level 2
SR1, COM1-02-06
closeThis is a distinguished talk as part of the NUS Computer Science Research Week 2019 (http://researchweek.comp.nus.edu.sg).
Abstract:
The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function.
In this talk, I???ll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving constrained regression problems over large datasets.
The talk starts with a tutorial on the preliminaries and the theoretical foundations of this topic.
Biodata:
Graham Cormode is a Professor in Computer Science at the University of Warwick in the UK, where he works on research topics in data management, privacy and big data analysis. Previously, he was a principal member of technical staff at AT&T Labs-Research. His work has attracted over 12,000 citations in the literature and has appeared in over 100 conference papers, 40 journal papers, and been awarded 30 US Patents.
Cormode is the co-recipient of the 2017 Adams Prize for Mathematics for his work on Statistical Analysis of Big Data. He has also edited two books on applications of algorithms to different areas, and co-authored a third.