arXiv:2602.07875v3 Announce Type: replace Abstract: Generating tabular data under conditions is critical to applications requiring precise control over the generative process. Existing methods rely on training-time strategies that do not generalise to unseen constraints during inference, and struggle to handle conditional tasks beyond tabular imputation. While manifold theory offers a principled way to guide generation, current formulations are tied to specific inference-time objectives and are limited to continuous domains. We extend manifold theory to tabular data and expand its scope to han
The proliferation of diffusion models combined with the increasing need for precise data generation in various applications has driven research towards more controllable and generalizable methods for tabular data.
This development allows for more controlled and nuanced tabular data generation, crucial for industries requiring accurate synthetic data for tasks like simulation, privacy-preserving data sharing, and complex systems modeling.
The ability to condition tabular data generation with unseen constraints robustly changes the landscape for synthetic data, moving past reliance on training-time strategies towards more adaptable, manifold-guided approaches.
- · AI researchers
- · Data scientists
- · Financial services
- · Healthcare
- · Traditional statistical data generation methods
- · Systems limited to continuous data generation
Improved synthetic data quality and utility for tabular datasets under complex conditional requirements.
Accelerated development of AI models that rely on sophisticated tabular data augmentation and anonymization.
Potential for new business models around highly customizable and privacy-preserving synthetic data generation services.
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Read at arXiv cs.LG