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Estimating properties of discrete distributions is a fundamental problem instatistical learning. We design the first unified, linear-time, competitive,property estimator that for a wide class of properties and for all underlyingdistributions uses just $2n$ samples to achieve the performance attained by theempirical estimator with $n\sqrt{\log n}$ samples. This provides off-the-shelf,distribution-independent, "amplification" of the amount of data availablerelative to common-practice estimators.  We illustrate the estimator's practical advantages by comparing it toexisting estimators for a wide variety of properties and distributions. In mostcases, its performance with $n$ samples is even as good as that of theempirical estimator with $n\log n$ samples, and for essentially all properties,its performance is comparable to that of the best existing estimator designedspecifically for that property.

Data Amplification: A Unified and Competitive Approach to Property Estimation