从数据核空间视角，实现对黑盒生成模型的统计推断。
Statistical inference on black-box generative models in the data kernel perspective space
- 将黑盒生成模型映射到数据核空间，构建可计算的模型表征。
- 在无模型权重与训练数据的前提下，成功完成多类模型级推断任务。
- 适用于模型评估、比较与溯源，尤其适合缺乏模型内部信息的场景。
生成模型在多个领域和主题上能够生成接近人类专家水平的内容。随着生成模型的影响日益扩大，开发统计方法以理解现有模型集合变得尤为重要。这些方法在用户无法获取模型预训练数据、权重或其他相关模型级协变量信息的场景中尤为关键。本文扩展了近期关于黑盒生成模型表示的研究成果，将其应用于模型级统计推断任务。我们证明，这种模型级表示在多种推断任务中均表现有效。
原文摘要 · Abstract (English)
Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop statistical methods to understand collections of available models. These methods are particularly important in settings where the user may not have access to information related to a model's pre-training data, weights, or other relevant model-level covariates. In this paper we extend recent results on representations of black-box generative models to model-level statistical inference tasks. We demonstrate that the model-level representations are effective for multiple inference tasks.
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