Convergence Rates for Neural-Network Estimation with Current-Status Data
arXiv 2606.10119•e3be7b83cd0676e268d37ae7ef732f4fda7c8fec7b66fd99aced0c429fb82b99
Boltzmann marginGromov-WassersteinItô mapSDEsactive learningcalibrationconformal predictioncurrent-status datadecision-calibrated uncertaintydiffusion/generative modelsdistributional shiftfederated learninggraph classificationheteroscedasticityhuman-AI teamingk-NNnear-exponential ratesneural networkspartition matroidpolar clusteringprobabilistic programming','program-based posterior training','Prange regularizationsieve MLEsubmodular optimizationtext-to-SQL
Paper metadata
- arXiv ID
- 2606.10119
- 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
- e3be7b83cd0676e268d37ae7ef732f4fda7c8fec7b66fd99aced0c429fb82b99
- Enrichment time
- 2026-06-10T07:23:59Z
- AI-assisted enrichment
- Yes
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