arXiv (All)
open access
Sudakov Minoration for Unconditional Log-Concave Vectors
Sudakov minoration asks whether a large family of separated random linear forms must have a large expected maximum. We present a proof candidate for this principle for all unconditional log-concave vectors. The argument starts with a classical reduction to sparse coordinate supports. It then selects
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
- Sudakov minoration for unconditional log-concave vectors with negatively associated magnitudes#1104724
- Noisy-Channel Minimum Bayes Risk Decoding#343487
- UMVUE-Type Estimators under Bregman Losses#800018
- Stein Kernels and Normal Approximation for Log-Concave Bilinear Forms#819474
- Stein Kernels and Normal Approximation for Log-Concave Bilinear Forms#809478
Record · ID 1115738
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