Geometric aspects of Statistical Learning Theory
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I will survey the progress made on one of the main questions in Statistical Learning Theory – the prediction problem. I will show that this problem has strong (and surprising) connections to high dimensional geometry and explain why the methods that have been developed for its solution are of independent interest in other areas of mathematics.
This talk is part of the Statistics series.
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