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.
The high-d land- scapes paradigm: spin-glasses, and beyond
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Any non-reciprocal component in high-dimensional dynamical systems drives the dynamics onto a chaotic attractor, with maximal Lyapunov exponent non-monotonic in the non-reciprocity degree.
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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.