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We present a novel algorithm, Random Conic Pursuit, that solves semidefinite programs (SDPs) via repeated optimization over randomly selected two-dimensional subcones of the PSD cone. This scheme is simple, easily implemented, applicable to very general SDPs, scalable, and theoretically interesting. Its advantages are realized at the expense of an ability(More)
Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions.The quantity of data and limitations of disk and memory bandwidth often make it infeasible to deliver answers at interactive speeds. However, it has been widely observed that many(More)
The bootstrap provides a simple and powerful means of assessing the quality of esti-mators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates features of both the bootstrap and(More)
As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality(More)
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