Adaptive Norm-Based Regularization for Neural Networks
2026-05-04T07:24:03Z•cd1ae8d8a0c05461636bc3608c23c1ba8b334fb9967fd017b19544fb963d14f0
Fisher-RaoGBDTNewton-boostingadaptive-samplingadjoint-methodsarxivdiffusion-modelsdose-responsedouble-machine-learningextreme-value-theoryflow-modelsglobal-convergence','adaptive-querying','persona-models','user-8gradient-boostinggraph-diffusioninformation-geometrylassomachine-learningregularizationreward-fine-tuningridgerobust-statisticsscore-matchingshift-estimatorsparsitystable-diffusion
What happened
This document is an arXiv stat/ml feed (multiple 04-May-2026 submissions) covering new methods in regularization, robust estimation, generative sampling, adaptive querying, decentralized sampling, boosting, and quantum ML. Key contributions: (1) Adaptive Norm-Based Regularization — covariance-aware L2/L1 penalties for neural nets improving prediction and structured sparsity in high-dimensional correlated features; (2) SHIFT — a robust double-machine-learning estimator for Average Dose-Response Functions that resists heavy-tailed/localized contamination and includes EVT diagnostics; (3) Unified
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- cd1ae8d8a0c05461636bc3608c23c1ba8b334fb9967fd017b19544fb963d14f0
- Enrichment time
- 2026-05-04T07:24:03Z
- AI-assisted enrichment
- Yes
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