Introduces random smoothing to produce asymptotically normal estimators and Wald confidence regions for linear regression with jointly stationary-ergodic errors without long-run variance estimation.
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2026 2verdicts
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Inferring data distributions precisely allows distilling exact unlearning signals, yielding KL divergence bounds to the retrained model and outperforming competitors in three forgetting scenarios.
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New Confidence Regions for Linear Regression Parameters with Stationary-Ergodic Dependent Errors
Introduces random smoothing to produce asymptotically normal estimators and Wald confidence regions for linear regression with jointly stationary-ergodic errors without long-run variance estimation.
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Exact Unlearning from Proxies Induces Closeness Guarantees on Approximate Unlearning
Inferring data distributions precisely allows distilling exact unlearning signals, yielding KL divergence bounds to the retrained model and outperforming competitors in three forgetting scenarios.