| Abstract |
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Recent advances of analysis on the hypercube have found some
surprising applications in Banach space theory, computational learning
theory, random graphs, and social choice. I will present several
examples including Enflo's problem, Talagrand's inequality, and
learning low-degree functions: how many values of an unknown function
f on n boolean inputs one needs to know in order to efficiently
approximate the function? The talk is based on joint works with D.
Beltran, A. Eskenazis, R. van Handel, J. Madrid, Y. Stone, and A.
Volberg.
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