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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2 Pith papers cite this work. Polarity classification is still indexing.
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A benchmark of 29 emulators on 100 datasets shows no single method wins everywhere and introduces the duqling R package to standardize future surrogate comparisons.
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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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All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogates
A benchmark of 29 emulators on 100 datasets shows no single method wins everywhere and introduces the duqling R package to standardize future surrogate comparisons.