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Generalization Bounds for Dependent Data using Online-to-Batch Conversion
We show that the generalization error of statistical learners in the dependent data setting is equivalent to the generalization error of statistical learners in the i. i. d.
Approximating Probability Distributions by ReLU Networks
We also obtain a lower bound on the minimum number of neurons needed to approximate the histogram distributions.
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