We explain how to use concepts from learning theory to make optimal auction theory more operational, replacing the “common prior” assumption with a data-driven approach.  For example, we prove that in arbitrary single-parameter settings, one can learn an auction with expected revenue arbitrarily close to optimal from a polynomial number of samples from an unknown valuation distribution.