Decentralized Conformal Novelty Detection via Quantized Model Exchange
arXiv 2605.08263•67c033475aa265c32dde63dc447ea396efd875b4077a3386165af4e46eed1acf
causal-inferenceconformal-predictiondecentralized-learningdistributed-algorithmsfalse-discovery-ratefixed-point-methodsgromov-wassersteinhealthcare-MLindependent-component-analysisinverse-optimizationlabel-efficiencymachine-learningmode-separationnormalizing-flowsnovelty-detectionoptimal-transportprivacyquantizationrepresentation-learningscore-based-modelssinkhornstatisticssurvey-weightingtransformer-theory
Paper metadata
- arXiv ID
- 2605.08263
- 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
- 67c033475aa265c32dde63dc447ea396efd875b4077a3386165af4e46eed1acf
- Enrichment time
- 2026-05-12T07:23:55Z
- 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.