Hugging Face Trending Papers
Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
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arXiv Machine Learning
Oct 7
Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
arXiv:2610.07721v1 Announce Type: cross Abstract: 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 a...
arXiv Machine Learning
Sep 10
A Geometric Phase Boundary for Volume-Sampled Linear Readouts
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.
arXiv Machine Learning
Oct 7
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
Aug 28
When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares
The paper extends prior work on volume sampling by providing a Loewner envelope for the centered coefficient covariance in least‑squares regression with a fixed pool of features and responses. It characterizes when this envelope is tight, linking tightness to strict spectral properties of residuals, and introduces a residual‑augmented change of measure to derive a one‑sided slack bound. The results also offer geometric insights at the boundary and demonstrate non‑vacuous certificates through frozen‑feature examples, focusing on conditional centered, full‑Gram‑whitened covariance rather than population generalization.
arXiv Machine Learning
Sep 10
Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling
arXiv:2609.06394v1 Announce Type: cross Abstract: Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computat...
arXiv Machine Learning
Sep 14
Block-Norm Geometries for Online Mirror Descent with Sparse Losses
The paper investigates how the choice of geometry in online mirror descent affects performance, particularly when loss gradients are sparse. It introduces randomized block‑norm mirror maps that interpolate between Euclidean and entropic geometries, achieving polynomial‑in‑dimension regret improvements over standard methods for various convex sets. The authors also demonstrate that naive alternation between mirror maps can lead to linear regret and propose a Hedge‑based meta‑algorithm that competes with the best mirror map in a finite portfolio, achieving near‑optimal regret for random block geometries.