  This paper proves the tight sample complexity of {\sf Second-Price Auction
with Anonymous Reserve}, up to a logarithmic factor, for each of all the value
distribution families studied in the literature: $[0,\, 1]$-bounded, $[1,\,
H]$-bounded, regular, and monotone hazard rate (MHR). Remarkably, the
setting-specific tight sample complexity $\mathsf{poly}(\varepsilon^{-1})$
depends on the precision $\varepsilon \in (0, 1)$, but not on the number of
bidders $n \geq 1$. Further, in the two bounded-support settings, our learning
algorithm allows {\em correlated} value distributions.
  In contrast, the tight sample complexity $\tilde{\Theta}(n) \cdot
\mathsf{poly}(\varepsilon^{-1})$ of {\sf Myerson Auction} proved by Guo, Huang
and Zhang (STOC~2019) has a nearly-linear dependence on $n \geq 1$, and holds
only for {\em independent} value distributions in every setting.
  We follow a similar framework as the Guo-Huang-Zhang work, but replace their
information theoretical arguments with a direct proof.
