Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs
arXiv 2605.24072•59e2fdb856c6b9448855b759d8b9211939db3d2688bce126bd41a3310f1b8592
Bayesian-inferenceEdgeworth-expansionautoencodersboostingcausal-inferenceconvex-clusteringdimensionality-reductiondo-calculusfairnessfinancial-clusteringfinite-widthinvariant-risk-minimizationmachine-learningmanifold-learningmetastabilitymodel-validationmulticalibrationneural-networksout-of-distribution-generalizationpolicy-optimization','single-index-model','stochastic-mirror-desrandom-walksreward-modelingstochastic-dominancetheorytrajectory-discrimination
Paper metadata
- arXiv ID
- 2605.24072
- 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
- 59e2fdb856c6b9448855b759d8b9211939db3d2688bce126bd41a3310f1b8592
- Enrichment time
- 2026-05-26T07:23:54Z
- AI-assisted enrichment
- Yes
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