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[2602.07875] Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion
Summary
The paper introduces HARPOON, a novel method for generating tabular data using generalized manifold guidance, addressing limitations in existing conditional generation techniques.
Why It Matters
HARPOON enhances the generation of tabular data by utilizing manifold theory, which allows for better handling of diverse conditional tasks. This advancement is crucial for applications requiring precise data generation and can significantly improve performance in various machine learning tasks.
Key Takeaways
- HARPOON extends manifold theory to improve tabular data generation.
- The method addresses limitations of existing conditional generation techniques.
- Empirical validation shows strong performance in tasks like imputation.
- Manifold-aware guidance enhances the ability to meet diverse constraints.
- HARPOON is applicable across various datasets, showcasing its versatility.
Computer Science > Machine Learning arXiv:2602.07875 (cs) [Submitted on 8 Feb 2026 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion Authors:Aditya Shankar, Yuandou Wang, Rihan Hai, Lydia Y. Chen View a PDF of the paper titled Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion, by Aditya Shankar and 3 other authors View PDF HTML (experimental) 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 handle diverse inference-time objectives. On this foundation, we introduce HARPOON, a tabular diffusion method that guides unconstrained samples along the manifold geometry to satisfy diverse tabular conditions at inference. We validate our theoretical contributions empirically on tasks such as imputation and enforcing inequality constraints, demonstrating HARPOON'S strong performance across diverse datasets and the practical benefits of manifold-aware guidance for tabular data. Code URL: t...