Learning joint quantizers for reconstruction and prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider the problem of empirical design of variable-rate quantizers for reconstruction and prediction. When a discriminative model (conditional distribution of the unobserved output given the observed input) is known or can be accurately estimated from a separate training set, we show that this problem reduces to designing a certain type of a generalized quantizer by means of empirical risk minimization on unlabeled input samples only. We derive a high-probability upper bound on the resulting expected performance of such a quantizer in terms of the training sample size and the complexity parameters of the reconstruction and the prediction problems. We also discuss two illustrative examples: binary classification with absolute loss and the information bottleneck.

Original languageEnglish (US)
Title of host publication2013 IEEE Information Theory Workshop, ITW 2013
StatePublished - 2013
Event2013 IEEE Information Theory Workshop, ITW 2013 - Seville, Spain
Duration: Sep 9 2013Sep 13 2013

Publication series

Name2013 IEEE Information Theory Workshop, ITW 2013


Other2013 IEEE Information Theory Workshop, ITW 2013

ASJC Scopus subject areas

  • Information Systems

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