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arXiv · 2604.02482

SEDGE: Structural Extrapolated Data Generation

Kun Zhang·Jiaqi Sun·Yiqing Li·Ignavier Ng·Namrata Deka·Shaoan Xie
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Abstract

This paper aims to address the challenge of data generation beyond the training data and proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable assumptions on the underlying data-generating process. We provide conditions under which data satisfying novel specifications can be generated reliably, together with the approximate identifiability of the distribution of such data under certain ``conservative" assumptions, as well as the inherent non-identifiability of this distribution without such assumptions. On the algorithmic side, we develop practical methods to achieve extrapolated data generation, based on a structure-informed optimization strategy or diffusion posterior sampling, respectively. We verify the extrapolation performance on synthetic data and also consider extrapolated image generation as a real-world scenario to illustrate the validity of the proposed framework.

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Kun Zhang, Jiaqi Sun, Yiqing Li, Ignavier Ng, Namrata Deka, Shaoan Xie. 2026-04-02. SEDGE: Structural Extrapolated Data Generation. https://arxiv.org/abs/2604.02482

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