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Uncertainty in loss distributions cripples standard risk-averse ML; this work shows credal sets can reliably prevent catastrophic decisions without sacrificing average performance.
Credal sets, previously impractical for large models, are now efficiently computable via a "decalibration" method that delivers strong performance in uncertainty-aware tasks.
