arXiv (All)
open access
Adaptive Scenario Generation for KL-DRO Around a Learned Density Model
Distributionally robust optimization (DRO) hedges data-driven two-stage stochastic programs against error in the distribution estimated from a finite record, and standard ambiguity sets differ in how they treat realizations absent from that record. A divergence ball around the empirical distribution
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
- Adaptive Scenario Generation for KL-DRO Around a Learned Density Model#1482985
- Learning, fast and slow: a two-fold data-based model adaptation algorithm for processes under varying operating conditions#1014530
- Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models#1343443
- Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models#1233913
- tidyHEBO: Improving the Robustness of Heteroskedastic Evolutionary Bayesian Optimization#1473706
Record · ID 1533427
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