α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
Matematicheskii Sbornik , volume=
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TARCO corrects measurement-error-induced correlated contamination in tree-aggregated compositional regression via bias-corrected estimating equations, tree-aware PSD stabilization, and sparse regularization, with finite-sample bounds and sign consistency.
Eigenvalues of Haar-random matrices over Z_p are asymptotically evenly distributed among algebraic extensions of Q_p by degree, with all but a bounded expected number lying in the maximal unramified extension Q_p^un; analogous results hold for roots of random Haar polynomials over Z_p.
citing papers explorer
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$\alpha$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
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Tree-aggregated regression for compositional data with measurement errors
TARCO corrects measurement-error-induced correlated contamination in tree-aggregated compositional regression via bias-corrected estimating equations, tree-aware PSD stabilization, and sparse regularization, with finite-sample bounds and sign consistency.
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Eigenvalue Distribution of $p$-adic Random Matrices Among Algebraic Extensions, with an Analogue for $p$-adic Random Polynomials
Eigenvalues of Haar-random matrices over Z_p are asymptotically evenly distributed among algebraic extensions of Q_p by degree, with all but a bounded expected number lying in the maximal unramified extension Q_p^un; analogous results hold for roots of random Haar polynomials over Z_p.