A condition-number principle shows that small suboptimality in admissible prototype clustering objectives implies small misclassification error when the condition number is low, with phase transitions for exact recovery.
Convergence of depth contours for multivariate datasets , volume =
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Proves reverse Poincaré inequality on global attractor of 2D reaction-diffusion system to obtain near-parametric statistical recovery of initial conditions from discrete observations.
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The Condition-Number Principle for Prototype Clustering
A condition-number principle shows that small suboptimality in admissible prototype clustering objectives implies small misclassification error when the condition number is low, with phase transitions for exact recovery.
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On statistical inference for non-linear dynamical systems evolving in their global attractor
Proves reverse Poincaré inequality on global attractor of 2D reaction-diffusion system to obtain near-parametric statistical recovery of initial conditions from discrete observations.