Optimal Distributed Online Prediction Using Mini-Batches

author: Lin Xiao, Microsoft Research
published: Jan. 13, 2011,   recorded: December 2010,   views: 4612


Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.


Online prediction methods are typically presented as serial algorithms running on a single processor. However, in the age of web-scale prediction problems, it is increasingly common to encounter situations where a single processor cannot keep up with the high rate at which inputs arrive. In this work we present the distributed mini-batch algorithm, a method of converting any serial gradient-based online pre- diction algorithm into a distributed algorithm that scales nicely to multiple cores, clusters, and grids. We prove a regret bound for this method that is asymptotically optimal for smooth convex loss functions and stochastic inputs. Moreover, our regret analysis explicitly takes into account communication latencies that occur on the network. Our method can also be adapted to optimally solve the closely- related distributed stochastic optimization problem.

See Also:

Download slides icon Download slides: nipsworkshops2010_xiao_odo_01.pdf (187.6┬áKB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: