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"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1407" "Title:","On Communication Cost of Distributed Statistical Estimation and Dimensionality" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper investigates the communication cost of distributed estimation for d-dimensional spherical Gaussian distribution with unknown mean and unitary covariance, where the joint distribution is assumed to be a product distribution of each coordinate. The authors generalize previous works on the one-dimensional case in [4] by proposing upper and lower bounds for d-dimensional data on two communication schemes, interactive and simultaneous communication settings, for achieving minimax squared loss. The results establish the tradeoffs between dimensionality and communication cost for distributed estimation. In addition, improved bounds are derived when the unknown mean is s-sparse.