When Kernel Ridge Regression Meets the H\"older-Zygmund Class: Minimax Optimality and Failure of Properness

arXiv 2607.26065•baf511e2a719adee6c9d2e2f87bb044ef51d294d6a608660879754dbc5f71686
LLM agentsadaptive sensinganomaly detectionarXivdiffusion modelsdomain generalizationforecastingkernel methodsmachine learning researchphysics-informed learningreinforcement learningstatistical machine learning

Paper metadata

arXiv ID
2607.26065
Version
Not specified by this published record
Category
Statistics — Machine Learning (stat.ML)

The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.

Evidence and limitations

Source ID
arxiv_stat_ml
Record identifier
baf511e2a719adee6c9d2e2f87bb044ef51d294d6a608660879754dbc5f71686
Enrichment time
2026-07-30T07:23:49Z
AI-assisted enrichment
Yes

This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.

When Kernel Ridge Regression Meets the H\"older-Zygmund Class: Minimax Optimality and Failure of Properness · Baitaphish