Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
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Constrained log-optimal e-variables are obtained by post-processing the unconstrained optimal e-variable via an appropriate transformation.
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.
citing papers explorer
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The Threshold Breakdown Point
Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
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Optimal e-variables under constraints
Constrained log-optimal e-variables are obtained by post-processing the unconstrained optimal e-variable via an appropriate transformation.
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On efficient robust regression with subquadratic samples
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.