AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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MissCNF is introduced, which trains CNFs directly on incomplete data by maximizing the marginal likelihood of each partially observed sample, without discarding rows or constructing a completed dataset, and is compared with two common strategies for handling missing data.
This work improves on existing fixed surrogate approaches and on iterated variants of each, most clearly on Lotka--Volterra, where no baseline falls below 0.96 data-space C2ST while the methods reach 0.64-0.68 in three of four initial-prior settings.
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