Presenter: Leopoldo Sarra, Max Planck Institute for the Science of Light
Title: Renormalized Mutual Information for Artificial Scientific Discovery
We derive a well-defined "renormalized" version of mutual information that allows us to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other. This quantity can be used 1) to estimate how much information a macroscopic quantity carries about a system and 2) to produce new low-dimensional effective descriptions of a high-dimensional system. This approach enables the discovery of collective variables in physical systems, or, more in general, to extract deterministic features via information maximization, thus adding to the toolbox of artificial scientific discovery.
As usual, the format will be a short presentation followed by plenty of discussion.
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