Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
arXiv 2605.18927•544a3f3de4490af9026f6c434b91e8c892e05d87b6c51baf632f256f05a79446
bayesian-inferenceconformal-predictiondiffusion-modelsgeneralized-posteriorgenerative-modelsgraph-modelsheavy-tailed-distributionshigher-order-langevininformation-processing-capacitylagrangian-relaxationlatent-space-modelslink-predictionmachine-learningmarkov-chainsmemorizationmilpmodel-misspecificationoptimizationphase-type-distributionsphotonic-computingsgd-streaming','causal-discovery','dag-clustering','non-gaussianstochastic-gradient-ascentstructure-selectiontensor-networkstransported-beta
Paper metadata
- arXiv ID
- 2605.18927
- 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
- 544a3f3de4490af9026f6c434b91e8c892e05d87b6c51baf632f256f05a79446
- Enrichment time
- 2026-05-20T07:23:58Z
- AI-assisted enrichment
- Yes
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