Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
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A new matrix zonotope perturbation method with coefficient-space approximation enables faster and less conservative data-driven reachability analysis than prior CMZ or MZ approaches.
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Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
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Data-Driven Reachability Analysis Using Matrix Perturbation Theory
A new matrix zonotope perturbation method with coefficient-space approximation enables faster and less conservative data-driven reachability analysis than prior CMZ or MZ approaches.