arXiv (All)
open access
Minimax and Adaptive Covariance Matrix Estimation under Differential Privacy
Estimating covariance matrices is fundamental to a wide range of statistical applications. This paper studies minimax and adaptive estimation of high-dimensional covariance matrices under $ρ$-zero-concentrated differential privacy ($ρ$-zCDP) over three nested classes: the pointwise-decay class $\mat
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Differentially Private Sampling from Distributions via Wasserstein Projection#175142
- The Cost of Privacy: Rates of Convergence for Parameter Estimation with Differential Privacy#1036498
- Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning#609724
- Privacy Amplification Without Independence: How Far Negative Dependence Carries the Guarantees of Poisson Subsampling#822359
- Sort, Partition, Randomize: Optimal Binary Hypothesis Testing under Local Differential Privacy#266105
Record · ID 1104987
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive