The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
Arbitrariness lies beyond the fairness-accuracy frontier
2 Pith papers cite this work. Polarity classification is still indexing.
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Advocates treating social welfare from economics as an additional core criterion for ML design and use in social settings, complementing optimization, generalization, and expressivity.
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Rashomon Sets and Model Multiplicity in Federated Learning
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
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Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap
Advocates treating social welfare from economics as an additional core criterion for ML design and use in social settings, complementing optimization, generalization, and expressivity.