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*Abstract*: Learning algorithms for implicit generative models can optimize a variety of criteria
that measure how the data distribution differs from the implicit model distribution,
including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy
criterion. A careful look at the geometries induced by these distances on
the space of probability measures reveals interesting differences. In particular, we can
establish surprising approximate global convergence guarantees for the 1-Wasserstein
distance, even when the parametric generator has a nonconvex parametrization.

Léon Bottou, Martin Arjovsky, David Lopez-Paz and Maxime Oquab: **Geometrical Insights for Implicit Generative Modeling**, To appear. ArXiV:1712.07822.

@incollection{bottou-geometry-2018, author = {Bottou, L{\'e}on and Arjovsky, Martin and Lopez-Paz, David and Oquab, Maxime}, title = {Geometrical Insights for Implicit Generative Modeling}, year = {2018}, note = {To appear. ArXiV:1712.07822}, url = {http://leon.bottou.org/papers/bottou-geometry-2018}, }

papers/bottou-geometry-2018.1525976089.txt.gz · Last modified: 2018/05/10 14:14 by leonb