arXiv (All)
open access
Minimax-Optimal Predictive Flow Matching under Distribution Drift
Flow matching learns a generative velocity field by regression, but under distribution drift a field must be deployed before the current data arrive. We study this predictive problem for a fixed, possibly nonconvex, neural class, with dynamic regret against the best represented field of each round.
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
- Euclidean Distance-Based Flow Matching#1344036
- VTV-FM: Flow Matching through Variational Terminal-Velocity Closure#1155720
- Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching#1116763
- Prediction--Loss Alignment for Sampler--Robust Flow Matching Training#998105
- Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representations#927808
Record · ID 1413139
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