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Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. SAME (State Augmentation for Marginal Estimation) [15, 8] is an approach to MAP parameter estimation which gives improved parameter estimates over direct Gibbs sampling. SAME can(More)
— Many systems have been developed for machine learning at scale. Performance has steadily improved, but there has been relatively little work on explicitly defining or approaching the limits of performance. In this paper we describe the application of roofline design, an approach borrowed from computer architecture, to large-scale machine learning. In(More)
Incremental model-update strategies are widely used in machine learning and data mining. By " incremental update " we refer to models that are updated many times using small subsets of the training data. Two well-known examples are stochastic gradient and MCMC. Both provide fast sequential performance and have generated many of the best-performing methods(More)
—Many large datasets exhibit power-law statistics: The web graph, social networks, text data, clickthrough data etc. Their adjacency graphs are termed natural graphs, and are known to be difficult to partition. As a consequence most distributed algorithms on these graphs are communication-intensive. Many algorithms on natural graphs involve an Allreduce: a(More)
Search advertising shows trends of vertical extension. Vertical ads typically offer better Return of Investment (ROI) to advertisers as a result of better user engagement. However, campaign and bids in vertical ads are not set at the keyword level. As a result, the matching between user query and ads suffers low recall rate and the match quality is heavily(More)
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