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.
Structural reducibility of multilayer networks
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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T-GINEE combines CP tensor decomposition with a generalized estimating equation framework and task-specific loss to explicitly model inter-layer correlations in multilayer graphs while providing consistency and asymptotic normality guarantees under mild conditions.
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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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T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
T-GINEE combines CP tensor decomposition with a generalized estimating equation framework and task-specific loss to explicitly model inter-layer correlations in multilayer graphs while providing consistency and asymptotic normality guarantees under mild conditions.