This paper analyzes the error rates of triangular transport maps used in conditional generative models. Researchers found that minimizing a joint Maximum Mean Discrepancy (MMD) can lead to inconsistent conditional measures, especially in misspecified parametric settings. An alternative estimator that directly targets conditional MMD is proposed, with a non-asymptotic error bound that decomposes into approximation, estimation, and reference sampling errors. The study suggests that focusing on conditional error, rather than joint error, is crucial for training conditional generative models effectively. AI
IMPACT Highlights potential issues in training conditional generative models, suggesting a need for revised approaches to improve accuracy.
RANK_REASON The cluster contains an academic paper detailing a new analysis of existing methods in conditional generative models. [lever_c_demoted from research: ic=1 ai=1.0]
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