The unreasonable effectiveness of the convexity assumption in high dimensions
This lecture surveys recent advances in high-dimensional convex geometry and probability, focusing on how convexity assumptions yield concentration of measure phenomena. Key results discussed include the resolution of Bourgain’s slicing problem and the Variance Conjecture, alongside progress on the isoperimetric problem for high-dimensional convex bodies.
MathematicsProbability & Information TheoryConvex GeometryHigh-Dimensional ProbabilityConcentration of Measure
Bo'az Klartag (Tel Aviv University) Beyond Graph Laplacians
Source: Simons Institute for the Theory of Computing via YouTubeWatch on YouTube
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