probability measure

Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem NeurIPS 2019 Probabilistic Multilevel Clustering via Composite Transportation Distance AISTATS 2019 Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning ICML 2019 Stochastic Deep Networks ICML 2019 Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions JMLR 2018 Policy Optimization as Wasserstein Gradient Flows ICML 2018 Multilevel Clustering via Wasserstein Means ICML 2017 Robust and Scalable Bayes via a Median of Subset Posterior Measures JMLR 2017 Fast Two-Sample Testing with Analytic Representations of Probability Measures NeurIPS 2015 Learning with a Wasserstein Loss NeurIPS 2015 Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric NeurIPS 2015 Towards an Axiomatic Approach to Hierarchical Clustering of Measures JMLR 2015 Fast Computation of Wasserstein Barycenters ICML 2014 Recovering Distributions from Gaussian RKHS Embeddings AISTATS 2014 Sketching the Support of a Probability Measure AISTATS 2014 Learning from Distributions via Support Measure Machines NeurIPS 2012 Learning Probability Measures with respect to Optimal Transport Metrics NeurIPS 2012 Recovery of Sparse Probability Measures via Convex Programming NeurIPS 2012 Hilbert Space Embeddings and Metrics on Probability Measures JMLR 2010 On the relation between universality, characteristic kernels and RKHS embedding of measures AISTATS 2010 Universal Kernels on Non-Standard Input Spaces NeurIPS 2010 Nonextensive Information Theoretic Kernels on Measures JMLR 2009 Learning Minimum Volume Sets JMLR 2006