empirical centralization
Country:
- North America > United States > New York (0.04)
- North America > United States > Rhode Island > Providence County > Providence (0.04)
- North America > United States > Massachusetts > Hampshire County > Amherst (0.04)
- (4 more...)
Technology:
Country:
- North America > United States > New York (0.04)
- North America > United States > Rhode Island > Providence County > Providence (0.04)
- North America > United States > Massachusetts > Hampshire County > Amherst (0.04)
- (4 more...)
Technology:
Review for NeurIPS paper: Sharp uniform convergence bounds through empirical centralization
Weaknesses: The authors consider the supremum of absolute deviation \sup_f \hat{E}_x[f]-E_D[f] . In statistical learning theory, it suffices to estimate \sup_{f}[E_D[f]-\hat{E}_x[f]], i.e., there is no absolute value. Therefore, the results in this paper may not be interesting since centralization does not help for a smaller complexity that is sufficient for generalization. The centralization in terms of expectation has been considered in the literature for both Rademacher averages and variances. This paper extends this centralization to empirical centralization.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.60)