  We present general principles for the design and analysis of unbiased Monte
Carlo estimators in a wide range of settings. Our estimators posses finite
work-normalized variance under mild regularity conditions. We apply our
estimators to various settings of interest, including unbiased optimization in
Sample Average Approximations, unbiased steady-state simulation of regenerative
processes, quantile estimation and nested simulation problems.
