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On efficient robust regression with subquadratic samples

cs.DS · 2026-05-18 · unverdicted · novelty 6.0

Near-linear time algorithm for robust regression under Gaussian covariates achieves O(sqrt(ε κ)) error with Õ(d/ε⁴) samples when ε κ ≲ 1, plus SQ and low-degree lower bounds.

A Semi-Supervised Kernel Two-Sample Test

stat.ML · 2026-05-03 · unverdicted · novelty 6.0

A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.

Spectral approximation for the separable covariance mixture model

math.ST · 2026-04-20 · unverdicted · novelty 6.0

Resolvents of the sample covariances in the separable mixture model approximate deterministic matrices defined via solutions to a dual system of equations, without simultaneous diagonalizability assumptions.

Tensor Cookbook: Mastering Tensors through Diagrams

cs.LG · 2026-05-15 · unverdicted · novelty 2.0

A guide presenting tensor algebra operations, decompositions, and gradients through tensor network diagrams for broader accessibility beyond quantum physics.

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Showing 4 of 4 citing papers after filters.

  • On efficient robust regression with subquadratic samples cs.DS · 2026-05-18 · unverdicted · none · ref 30

    Near-linear time algorithm for robust regression under Gaussian covariates achieves O(sqrt(ε κ)) error with Õ(d/ε⁴) samples when ε κ ≲ 1, plus SQ and low-degree lower bounds.

  • A Semi-Supervised Kernel Two-Sample Test stat.ML · 2026-05-03 · unverdicted · none · ref 173

    A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.

  • Spectral approximation for the separable covariance mixture model math.ST · 2026-04-20 · unverdicted · none · ref 130

    Resolvents of the sample covariances in the separable mixture model approximate deterministic matrices defined via solutions to a dual system of equations, without simultaneous diagonalizability assumptions.

  • Tensor Cookbook: Mastering Tensors through Diagrams cs.LG · 2026-05-15 · unverdicted · none · ref 40

    A guide presenting tensor algebra operations, decompositions, and gradients through tensor network diagrams for broader accessibility beyond quantum physics.