arXiv cs.CY· Federica Sibilla, Vasiliki Voukelatou, Duccio Piovani, Kyriacos Koupparis, Daniela Paolotti, Rossano Schifanella, Kyriaki Kalimeri·· 11 小时前AI 评分31
上下文条件生成模型实现稀疏人道主义调查的次国家级细化
Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys
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研究评估归一化流（normalizing flows）在严重数据稀缺下细化次国家级调查分布的能力，覆盖六个中低收入国家的八个人户调查数据集。上下文条件信息越丰富，模型性能系统性提升；该方法学习完整条件分布而非点估计，可为人道主义决策与资源分配提供细粒度证据。
正文 · 原文
Abstract:Data scarcity limits inference in many scientific and policy domains. Survey data are essential for decision-making, but sparse samples often fail to capture fine spatial granularities. We evaluate normalizing flows, a generative model that learns complex data distributions and can be conditioned on exogenous contextual features, in controlled data scarcity scenarios. Across eight household survey datasets spanning six low-income or middle-income countries in the humanitarian domain, we show that context-conditioned generative models can refine sub-national survey distributions under severe data scarcity, and that performance increases systematically with the richness of the conditioning information. These findings support a general principle for survey data augmentation: generative models can improve sub-national estimates when the sparse sample retains sufficient support and contextual covariates encode relevant local heterogeneity. By learning full conditional distributions rather than point estimates, the approach provides fine-grained evidence for humanitarian decision-making and resource allocation.
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2605.31489 [cs.CY] |
| (or arXiv:2605.31489v3 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2605.31489 arXiv-issued DOI via DataCite |
Submission history
From: Federica Sibilla [view email]
[v1]
Fri, 29 May 2026 16:12:24 UTC (3,569 KB)
[v2]
Thu, 4 Jun 2026 08:58:18 UTC (3,590 KB)
[v3]
Wed, 4 Nov 2026 16:01:05 UTC (3,812 KB)
来源：arXiv cs.CY · arxiv.org