This talk is concerned with probability measures in high dimension that satisfy certain geometric convexity assumptions. Probability distributions on high ...
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We are concerned with probability measures in high dimensions that satisfy certain geometric characteristics. • Are there any general, interesting principles?
Apr 24, 2010 ˇ Abstract. We review recent advances in the understanding of probability measures with geometric characteristics on Rn , for large n.
PDF | We discuss metric, algorithmic and geometric issues related to broadly understood complexity of high dimensional convex sets. The specific topics.
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We shall see that convexity conditions fit very well with the high dimensionality. Uniform measures on convex domains.
and, since these geometric parameters of random convex bodies are random variables, their expectation, variance, or whether their value is of some order ...
Mar 25, 2019 ˇ Theoretical analysis implies that convex penalization schemes have nearly optimal estimation properties in certain settings. However, in general ...
[PDF] Fundamental barriers to high-dimensional regression ... - NSF PAR
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This study supports the perhaps surprising conclusion that estimator (1.3) is suboptimal among polynomial-time estimators for the task of noiseless recovery of ...
Description: This course will address the design of provably efficient algorithms for data processing that leverage prior information. We will focus on the ...