arXiv (All)
open access
Sample-Preserving Search-to-Decision Reduction for Noisy Linear Equations over Large Moduli
The Noisy Linear Equations problem involves finding the solution to a random linear system over a finite field given noisy evaluations. This is a generalisation of various learning problems widely used in cryptography, including Learning with Errors (LWE), Learning with Rounding (LWR), and Learning
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
- Sample-Preserving Search-to-Decision Reduction for Noisy Linear Equations over Large Moduli#1291741
- Deriving Approximate Message Passing from the Convex Gaussian Min-Max Theorem#1342543
- Learning Sparse Support under Differential Privacy: Adaptive Algorithms and Minimax Limits#1202286
- A method for the solution of certain non-linear problems in least squares#6874
- Strongly Refuting Semirandom Linear Systems in Subexponential Time#1078123
Record · ID 1297265
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