  We study a new technique for understanding convergence of learning agents
under small modifications of data. We show that such convergence can be
understood via an analogue of Fatou's lemma which yields gamma-convergence. We
show it's relevance and applications in general machine learning tasks and
domain adaptation transfer learning.
