On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
2026-05-18T07:23:54Z•f2e6d1eeee4ee17f5b1c60c8953240019bc3ea2bdf2a273a7dac93d47289ad31
Lipschitz risk functionalsMaxSketchTCAValgorithmic contestability and recourse methods','neural networkconcept activation vectorscontextual banditscovariance queriesdistinct countingdomain shift detectioneigenalignmentexplainabilityfinite-sample analysisgeneralization boundsgraphical modelskernel ridge regressionoffline contextual banditsoracle price maprandom projectionsregret boundsrepresentation learningrisk-aware policy learningsemiparametric pricingstreaming algorithmssubspace attributionα-TCAV
What happened
Collection of recent ML/statistics papers (arXiv, 18 May 2026) covering: (1) theoretical analysis of kernel ridge regression linking generalization to eigenvector/eigenvalue estimation and eigenalignment; (2) semiparametric contextual pricing with a coarse-to-fine oracle-price learning policy and matching regret lower bounds; (3) MaxSketch, a random-projection sketch for robust distinct counting in noisy/high-dimensional learned representations with logarithmic-memory guarantees; (4) risk-aware offline policy learning for general Lipschitz risk functionals with minimax-optimal rates; (5) α-TCÁ
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- f2e6d1eeee4ee17f5b1c60c8953240019bc3ea2bdf2a273a7dac93d47289ad31
- Enrichment time
- 2026-05-18T07:23:54Z
- AI-assisted enrichment
- Yes
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