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, Iíll 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 data sets.
The talk starts with a tutorial on the preliminaries and the theoretical foundations of this topic.
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