Distributed Machine Learning and Matrix Computations

The emergence of large distributed matrices in many applications has brought with it a slew of new algorithms and tools. Over the past few years, machine learning and numerical linear algebra on distributed matrices has become a thriving field. Manipulating such large matrices makes it necessary to think about distributed systems issues such as communication cost.

This workshop aims to bring closer researchers in distributed systems and large scale numerical linear algebra to foster cross-talk between the two fields. The goal is to encourage distributed systems researchers to work on machine learning and numerical linear algebra problems, to inform machine learning researchers about new developments on large scale matrix analysis, and to identify unique challenges and opportunities. The workshop will conclude with a session of contributed posters.