arXiv Machine Learning
Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
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Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
We study ridge regression from exactly $s$ distinct rows of a fixed design. Responses are fixed, and only the subset is random. The determinant law and selected ridge fit share one positive definite p...
arXiv Machine Learning
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When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares
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The paper investigates volume‑sampled linear readouts with fixed feature pools and responses, focusing on the randomness introduced solely by subset selection. It establishes a globally sharp Loewner envelope for centered, full‑Gram‑whitened coefficient covariance and characterizes when a positive geometric margin exists versus when it vanishes, using conditions on residual covariance slack and a pairwise Naimark‑complement minor test. The results provide explicit geometric boundaries and conservative certificates for strictness and variance terms in fixed‑query squared loss, offering a design‑specific phase characterization for this randomized linear‑readout primitive.
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Contact Geometry and Covariance Deficits in Volume-Sampled Least Squares
arXiv:2608.26877v3 Announce Type: replace Abstract: We classify when ordinary fixed-size volume sampling followed by unweighted least squares attains its sharp coefficient-covariance ceiling on a fix...
arXiv Machine Learning
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High-dimensional Linear Bandits with Knapsacks
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