Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions

ArXi:2603.25948v1 Announce Type: cross Optimization problems routinely depend on uncertain parameters that must be predicted before a decision is made. Classical robust and regret formulations are designed to handle erroneous predictions and can provide statistical error bounds in simple settings. However, when predictions lack rigorous error bounds (as is typical of modern machine learning methods) classical robust models often yield vacuous guarantees, while regret formulations can paradoxically produce decisions that are optimistic than even a nominal solution. We