SEDGE: Structural Extrapolated Data Generation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913125323767808 |
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| author | Zhang, Kun Sun, Jiaqi Li, Yiqing Ng, Ignavier Deka, Namrata Xie, Shaoan |
| author_facet | Zhang, Kun Sun, Jiaqi Li, Yiqing Ng, Ignavier Deka, Namrata Xie, Shaoan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02482 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SEDGE: Structural Extrapolated Data Generation Zhang, Kun Sun, Jiaqi Li, Yiqing Ng, Ignavier Deka, Namrata Xie, Shaoan Machine Learning 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. |
| title | SEDGE: Structural Extrapolated Data Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.02482 |