arXiv:2605.06355v2 Announce Type: replace Abstract: Order-Agnostic autoregressive models have demonstrated strong performance in deep generative modeling, yet their use in settings with incomplete data remains largely unexplored. In this work, we reinterpret them through the lens of missing data. First, we show that their standard training procedure on fully observed data implicitly performs imputation under a missing completely at random mechanism, resulting in robust out-of-sample imputation performance in settings with high missingness. Second, we introduce the first principled framework fo
The increasing complexity and scale of generative AI models, coupled with real-world data imperfections, necessitate robust methods for handling incomplete datasets.
This development addresses a fundamental challenge in applying advanced generative AI, extending its utility to noisy and incomplete real-world data scenarios, which are prevalent in most practical applications.
AI models can now maintain strong performance and reliability with missing data, reducing the need for costly and time-consuming data cleaning or acquisition processes in many applications.
- · AI researchers and developers
- · Industries relying on imperfect datasets (e.g., healthcare, finance)
- · Companies building deep generative models
- · Data cleaning and preprocessing service providers (potentially reduced demand)
- · Traditional imputation methods with lower efficacy
Improved reliability and broader applicability of deep generative AI in real-world settings.
Accelerated development and deployment of AI-driven systems across sectors previously limited by data quality issues.
Potentially democratizes advanced AI usage by lowering the barrier of entry related to pristine data requirements.
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Read at arXiv cs.LG