Unverified50% confidenceFactExact time
The paper treats conditional samples as the positive class and the particles in the diffusion process as unlabeled data, training a lightweight classifier online to estimate the probability that each particle belongs to an undesirable condition.
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10/15/2026
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UnverifiedThe paper proposes using Positive-Unlabeled Learning to reformulate negative guidance, giving the sampling process a well-defined target density and achieving asymptotic exactness in the particle limit.70% similarUnverifiedWhen fine-tuning embedding models, hard negatives (such as positions in the same domain but at different levels) should be used instead of random negatives, in order to learn more fine-grained semantic differences.69% similarUnverifiedThe paper "Finite-Sample Guarantees for Fully Discrete Conditional Diffusion Models" establishes finite-sample error guarantees for conditional diffusion models that are learned from data and implemented with a finite number of denoising steps.69% similarUnverifiedThe paper demonstrates through practical scenarios that identification-invariant clustering can be applied in real causal inference tasks, and is applicable to complex variable network domains such as epidemiology, economics policy evaluation, and A/B experiment analysis.68% similarUnverifiedThe key insight of this study is that when the likelihood function derived from the data model is known and differentiable, guided diffusion modeling can be used to sample the predictive distribution.67% similar
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