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Testing Support Size More Efficiently Than Learning Histograms
The best known upper bound for problem (1) uses a general algorithm for learning the histogram of the distribution $p$, which requires $\Theta(\tfrac{n}{\epsilon^2 \log n})$ samples.
VC Dimension and Distribution-Free Sample-Based Testing
Conversely, we show that two natural classes of functions, juntas and monotone functions, can be tested with a number of samples that is polynomially smaller than the number of samples required for PAC learning.
Text-based inference of moral sentiment change
We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora.
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