T. Syed

CalTwin cuts out-of-distribution prediction errors by over 9%, tackling both covariate shift and overconfidence in medical world models with a single elegant solution.

PIcsC reduces fragmentation-induced performance degradation by over 20% while outperforming leading federated learning methods without requiring client-specific personalization.