Enes Ağırman

Iterative Robust Satisficing: Minimizing Performance Degradation Under Distribution Shift

Modern neural networks often achieve high accuracy on their training distribution but degrade sharply under distribution shifts. We address this problem through *Robust Satisficing* (RS), an optimization objective that seeks parameters which attain a target level of in-distribution performance while…