Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models
arXiv 2602.23518•b2b8ec07b54c256e5f15241f6eee72c2324f8429ed4286f3aedc52b22d764268
Hawkes-processactive-learningalgorithmic-fairnessalgorithmsarxiv-2026astrophysicsbayesian-policy-learningcalibrationcausal-discoverydisentanglementdistributed-optimizationf-divergencegenerative-modelsheteroscedasticityimportance-samplinginterpretabilitymachine-learningneural-operatorspartition-functionpoint-processesspatio-temporalsymbolic-regressiontheoryvariational-inference
Paper metadata
- arXiv ID
- 2602.23518
- 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
- b2b8ec07b54c256e5f15241f6eee72c2324f8429ed4286f3aedc52b22d764268
- Enrichment time
- 2026-03-04T20:01:20Z
- AI-assisted enrichment
- Yes
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