Robust Distributed Online Prediction

Details Robust Distributed Online Prediction Robust Distributed Online Prediction

2010-12-07

arxiv.org/abs/1012.1370v1

Computer Science

Learning

Scientific paper

The standard model of online prediction deals with serial processing of inputs by a single processor. However, in large-scale online prediction problems, where inputs arrive at a high rate, an increasingly common necessity is to distribute the computation across several processors. A non-trivial challenge is to design distributed algorithms for online prediction, which maintain good regret guarantees. In \cite{DMB}, we presented the DMB algorithm, which is a generic framework to convert any serial gradient-based online prediction algorithm into a distributed algorithm. Moreover, its regret guarantee is asymptotically optimal for smooth convex loss functions and stochastic inputs. On the flip side, it is fragile to many types of failures that are common in distributed environments. In this companion paper, we present variants of the DMB algorithm, which are resilient to many types of network failures, and tolerant to varying performance of the computing nodes.

Affiliated with

Also associated with

No associations

Rating

Robust Distributed Online Prediction does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Robust Distributed Online Prediction, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust Distributed Online Prediction will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-479441