  The principle of maximum entropy is a broadly applicable technique for
computing a distribution with the least amount of information possible
constrained to match empirical data, for instance, feature expectations. We
seek to generalize this principle to scenarios where the empirical feature
expectations cannot be computed because the model variables are only partially
observed, which introduces a dependency on the learned model. Generalizing the
principle of latent maximum entropy, we introduce uncertain maximum entropy and
describe an expectation-maximization based solution to approximately solve
these problems. We show that our technique additionally generalizes the
principle of maximum entropy. We additionally discuss the use of black box
classifiers with our technique, which simplifies the process of utilizing
sparse, large data sets.
