Justin Solomon

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  Sampling-Based Algorithms for Optimal Transport Problems
  Justin Solomon
  Sampling provides a common means of access to probability measures in Bayesian inference, streaming data processing, and other applications. In this setting, it is often impossible to access the distribution function or to make assumptions about the supports of the measures involved. In this talk, I will summarize some efforts in our research group to estimate optimal transport distances and solve derived optimization problems (e.g., barycenter estimation and barycentric regression) given only sample access to the input measures.