Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
arXiv 2605.22950•810a7395745c14dd49d2996775e535583958dd48f166a348111beaca8d71a7ff
Bradley-TerryDAG-learningKolmogorov-Arnold-networksLLM-priorsMahalanobis-metriccausal-inferencediffusion-modelsdouble-descentfeature-selectionhazard-estimationindividual-fairnessmachine-learningnoise-adaptivitypairwise-comparisonsparameter-estimationprivileged-informationscaling-lawsscore-matchingsemi-supervised-learningsparse-activationssparsitysparsity-priorspike-and-slabsurvival-analysisuncertainty-estimation','monte-carlo-dropout','dirichlet-models'
Paper metadata
- arXiv ID
- 2605.22950
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 810a7395745c14dd49d2996775e535583958dd48f166a348111beaca8d71a7ff
- Enrichment time
- 2026-05-25T07:23:56Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.