The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
The curious case of arbitrariness in machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
- An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness