Concave Statistical Utility Maximization Bandits via Influence-Function Gradients
arXiv 2604.22140•1ef7dfb240767cadeef148f4055605d351c5e4d26c9eed1fce3a8cdd5d997b7a
Nystrom approximationProjAvgRLAvgSPD matricesSQUEAKStiefel manifoldSuper LearnerWassersteinGradconcave utilityconformal predictionconformalized Super Learnerentropic Wasserstein barycenter','explainability','feature at-federated learninggeometry-aware aggregationinfluence functionkernel density/proposal learningkernel ridge regressionmirror ascentmulti-armed banditsmultiplicative weightspliable rejection samplingrejection samplingridge leverage scoresstochastic gradientsunnormalized RLS
Paper metadata
- arXiv ID
- 2604.22140
- 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
- 1ef7dfb240767cadeef148f4055605d351c5e4d26c9eed1fce3a8cdd5d997b7a
- Enrichment time
- 2026-04-27T07:24:07Z
- AI-assisted enrichment
- Yes
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