arXiv (All)
open access
Learning Computational Structure for Sample-Efficient Prediction
Statistical learning in high dimensions suffers from the curse of dimensionality. Fortunately, many functions of practical interest are compositionally sparse, meaning they admit a composition of local functions, each depending on only a few variables, allowing sample complexity to depend on local a
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
- Learning Computational Structure for Sample-Efficient Prediction#1394239
- Scalable and Robust Spatial Prediction via Multi-Resolution Ensembles of Predictive Processes#1187345
- SPATIAL REGRESSION MODELS USING INTER-REGION DISTANCES IN A NON-RANDOM CONTEXT#941678
- Beyond Conventional Federated Learning via High-Order Regularization#996717
- When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection#1076488
Record · ID 1412613
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