  This paper considers the design of non-truthful mechanisms from samples. We
identify a parameterized family of mechanisms with strategically simple
winner-pays-bid, all-pay, and truthful payment formats. In general (not
necessarily downward-closed) single-parameter feasibility environments we prove
that the family has low representation and generalization error. Specifically,
polynomially many bid samples suffice to identify and run a mechanism that is
$\epsilon$-close in Bayes-Nash equilibrium revenue or welfare to that of the
optimal truthful mechanism with high probability.
