This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Wednesday, December 9 • 19:00 - 23:59
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling

Sign up or log in to save this to your schedule and see who's attending!

We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distribution. In contrast to typical analysis, we directly bound the decrease of the primal-dual error (in expectation), without the need to first analyze the dual error. Depending on the choice of the sampling, we obtain efficient serial and mini-batch variants of the method. In the serial case, our bounds match the best known bounds for SDCA (both with uniform and importance sampling). With standard mini-batching, our bounds predict initial data-independent speedup as well as additional data-driven speedup which depends on spectral and sparsity properties of the data.

Wednesday December 9, 2015 19:00 - 23:59
210 C #85

Attendees (1)