The goal of this workshop is to advance the algorithmic frontier of machine learning. Target areas include Bayesian statistics, in which many of the core algorithmic problems bear similarity to problems that have been studied intensively in the theoretical computer science community; and large-scale optimization, in which a host of interesting challenges arise at the interface of theory and practical deployment.
The workshop will bring together researchers in algorithms, statistics, mathematics and artificial intelligence. It will be open to all potential participants, and the workshop findings (including videotapes of presentations) will be distributed to the public for comments and engagement. The organizers will encourage students to attend the workshop, and will actively recruit scientists from a diversity of backgrounds to contribute to a wide range of algorithmic topics.
