arXiv (OAI Expanded)
open access
Sample Average Approximation for Distributionally Robust Optimization with $ϕ$-divergences
It is well known that estimating the expectation of any given bounded random variable with values in $[-B, B]$ has two basic properties: (1) the number of samples needed scales at the order of $O(B^2/ε^2)$, where $ε$ is the prescribed target precision, and (2) such a sample complexity is independent
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 Average Approximation for Distributionally Robust Optimization with $ϕ$-divergences#924540
- Minimax and adaptive estimation of general linear functionals under sparsity#793489
- Minimax and adaptive estimation of general linear functionals under sparsity#789485
- Towards Weaker Variance Assumptions for Stochastic Optimization#1116095
- Convergence rate of the moment-SOS hierarchy for univariate polynomial optimization#1034358
Record · ID 920309
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