Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
arXiv 2603.12351•0e7e01d57467de9073e54b8e8b2ac9c84cbf3da187adce4f3ced8fe09f1142c9
3D-molecule-generationAlzheimer's-ADNIEM-algorithmL0-approximationProJIVERANSACRKHSVariational-GarroteVecMolbandit-regretbatched-banditsdata-integrationdiffusion-modelsenergy-based-modelskernelized-banditslearning-dynamicsmachine-learningneural-fieldsneuroimagingoffline-reinforcement-learning','BCPO'','conservative-RL'','Bay[probabilistic-PCAprobabilistic-modelsrobust-estimationsample-complexitysparse-inverse-problems
Paper metadata
- arXiv ID
- 2603.12351
- 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
- 0e7e01d57467de9073e54b8e8b2ac9c84cbf3da187adce4f3ced8fe09f1142c9
- Enrichment time
- 2026-03-16T07:24:17Z
- AI-assisted enrichment
- Yes
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