Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
2026-03-16T07:24:17Z•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
What happened
Collection of new machine‑learning research (arXiv:2603.*) spanning probabilistic data‑integration (ProJIVE: EM-based probabilistic JIVE for joint/individual variation with ADNI application and code), robust estimation (EB‑RANSAC: energy‑based alternative to RANSAC), sparsity for inverse problems (Variational Garrote vs L1), improvements and lower bounds for batched kernelized bandits, a novel vector‑field generative paradigm for 3D molecule generation (VecMol) with QM9/GEOM‑Drugs validation, a theoretical/empirical analysis of diffusion models’ learning dynamics and a “diffusion information‑
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 0e7e01d57467de9073e54b8e8b2ac9c84cbf3da187adce4f3ced8fe09f1142c9
- Enrichment time
- 2026-03-16T07:24:17Z
- 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.