## Data summarization and distributed computation, 2018.
Keynote talk at PODC 2018.

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.

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