Tighter Variational Representations of f-Divergences via Restriction to   Probability Measures
  We show that the variational representations for f-divergences currently used in the literature can be tightened. This has implications to a number of methods recently proposed based on this representation. As an example application we use our tighter representation to derive a general f-divergence estimator based on two i.i.d. samples and derive the dual program for this estimator that performs well empirically. We also point out a connection between our estimator and MMD. 
Avraham Ruderman (Australian National University and NICTA), Mark Reid   (Australian National University and NICTA), Dario G...
cs.LG stat.ML