Price of Fairness in Bandits: A Tight Minimax Characterization
arXiv 2607.13402•14c1132bee4e557e94af64de567159032708013e2d0c536fd0f2a3a4688cdffc
CANA-framework','graph-regularizationCoulomb-kernelEB-VAELLM-invocationMMDUCB-HAREWasserstein-gradient-flowanalogical-reasoningbanditsboostingconditional-distributionsevent-triggeringfairnesshistorical-analogieslongitudinal-modelingmachine-learningmaskingmodel-fingerprintingparallel-gradient-boostingprivacyregret-boundssequential-decisionstochastic-approximationtime-to-eventtwo-time-scale
Paper metadata
- arXiv ID
- 2607.13402
- 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
- 14c1132bee4e557e94af64de567159032708013e2d0c536fd0f2a3a4688cdffc
- Enrichment time
- 2026-07-16T07:23:58Z
- AI-assisted enrichment
- Yes
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