Abstract:We give an algorithmically efficient version of the learner-to-compression scheme conversion in . In extending this technique to realvalued hypotheses, we also obtain an efficient regression-to-bounded sample compression converter. To our knowledge, this is the first general compressed regression result (regardless of efficiency or boundedness) guaranteeing uniform approximate reconstruction. Along the way, we develop a generic procedure for constructing weak real-valued learners out of abstract regressors; th… Show more
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