Invited keynote talk at Principles of Distributed Computation (PODC) 2018 in July 2018 on “Data summarization and distributed computation”

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 robust regression problems over large, distributed data sets.