Fisher-Geometric Diffusion in Stochastic Gradient Descent: Optimal Rates, Oracle Complexity, and Information-Theoretic Limits
arXiv 2603.02417•41b479e34343a7244b289af2f9a280e058e9825edeb8da224d5e7d5759dd993f
Finsler-geometryFisher-informationGodambe-matrixGromov-WassersteinPOMDPs','belief-space-metrics','clustering','elbow-method','UMAPSHAPSPD-matricescategorical-datacausal-inferencecombinatorial-algorithmsconformal-predictiondensity-clusteringdifferential-geometrydiffusion-approximationfunctional-ANOVAgeneralized-Bayesgraphslow-degree-methodmachine-learningmartingale-posteriorrobust-subspace-recoverysparse-PCAstatisticsstochastic-gradient-descentuncertainty-quantification
Paper metadata
- arXiv ID
- 2603.02417
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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- Source ID
- arxiv_stat_ml
- Record identifier
- 41b479e34343a7244b289af2f9a280e058e9825edeb8da224d5e7d5759dd993f
- Enrichment time
- 2026-03-04T20:00:20Z
- AI-assisted enrichment
- Yes
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