The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
published: Sept. 27, 2015, recorded: July 2015, views: 1749
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. Unfortunately, the resulting submodular optimization problems are often too large to be solved on a single machine. We consider a distributed, greedy algorithm that combines previous approaches with randomization. The result is an algorithm that is embarrassingly parallel and achieves provable, constant factor, worst-case approximation guarantees. In our experiments, we demonstrate its efficiency in large problems with different kinds of constraints with objective values always close to what is achievable in the centralized setting.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !