The Theory behind UMAP?
arXiv 2603.03375•4bbc1c6bb98c39ae698c395b663340e30508394bd3300ef55a0847d8c47ad832
Riemannian-LangevinSurprisal-RenyiUMAPaggregation-methods','semi-supervised-learningarxivbanditsbenchmarkingcausal-discoveryclusteringdiffusion-modelsdimensionality-reductionensemblesexperimental-designgenerative-modelsgradient-descentinformation-theorykernel-methodsmax-plus-networksno-free-lunchresearchscore-matchingsoft-interventionssparse-autoencodersstat_mlstochastic-processes
Paper metadata
- arXiv ID
- 2603.03375
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 4bbc1c6bb98c39ae698c395b663340e30508394bd3300ef55a0847d8c47ad832
- Enrichment time
- 2026-03-05T07:24:05Z
- AI-assisted enrichment
- Yes
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