Information Bottleneck Methods for Distributed Learning

26 Oct 2018Parinaz FarajiparvarAhmad BeiramiMatthew Nokleby

We study a distributed learning problem in which Alice sends a compressed distillation of a set of training data to Bob, who uses the distilled version to best solve an associated learning problem. We formalize this as a rate-distortion problem in which the training set is the source and Bob's cross-entropy loss is the distortion measure... (read more)

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