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Title: Distributed and Stochastic Machine Learning on Big Data
Speaker: James Kwok
Abstract: On big data sets, it is often challenging to learn the parameters in a machine learning model. A popular technique is the use of stochastic gradient, which computes the gradient at a single sample instead of over the whole data set. Another alternative is distributed processing, which is particularly natural when a single computer cannot store or process the whole data set. In this talk, some recent extensions will be presented. For stochastic gradient, instead of using the information from only one sample, we incrementally approximate the full gradient by also using old gradient values from the other samples. It enjoys the same computational simplicity as existing stochastic algorithms, but has faster convergence. As for existing distributed machine learning algorithms, they are often synchronized and the system can move forward only at the pace of the slowest worker. I will present an asynchronous algorithm which requires only partial synchronization, and updates from the faster workers can be incorporated more often by the master.