Sampling through optimization of divergences on the space of measures

Abstract:
Sampling from a target measure when only partial information is available (e.g. unnormalized density as in Bayesian inference, or true samples as in generative modeling) is a fundamental problem in computational statistics and machine learning. The sampling problem can be cast as an optimization one over the space of probability distributions of a well-chosen discrepancy,  e.g. a divergence or distance to the target. In this talk, I will discuss several properties of sampling algorithms for some choices of discrepancies (standard ones, or novel proxies), both regarding their optimization and quantization aspects.

Bio:
Anna Korba is an assistant professor at ENSAE/CREST in the Statistics Department. Her main line of research is machine learning, and she has been working on kernel methods, optimal transport, optimization, particle systems and preference learning. At the moment, she is particularly interested in sampling and optimization methods. She received her PhD from Telecom ParisTech, under the guidance of Prof. Stephan Clémençon. Previously, she was a postdoctoral researcher with Arthur Gretton at University College London in the Gatsby Computational Neuroscience Unit.