@inproceedings{6773,
  title = {Non-parametric estimation of integral probability metrics},
  journal = {Proceedings of the IEEE International Symposium on Information Theory (ISIT 2010)},
  abstract = {In this paper, we develop and analyze a nonparametric
  method for estimating the class of integral probability
  metrics (IPMs), examples of which include the Wasserstein distance,
  Dudley metric, and maximum mean discrepancy (MMD).
  We show that these distances can be estimated efficiently by
  solving a linear program in the case of Wasserstein distance and
  Dudley metric, while MMD is computable in a closed form. All
  these estimators are shown to be strongly consistent and their
  convergence rates are analyzed. Based on these results, we show
  that IPMs are simple to estimate and the estimators exhibit good
  convergence behavior compared to fi-divergence estimators.},
  pages = {1428-1432},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  institution = {Institute of Electrical and Electronics Engineers},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = jun,
  year = {2010},
  slug = {6773},
  author = {Sriperumbudur, BK. and Fukumizu, K. and Gretton, A. and Sch{\"o}lkopf, B. and Lanckriet, GRG.},
  month_numeric = {6}
}