Caching with Calibrated Predictions
Learning-augmented online algorithms use predictions to improve performance beyond worst-case guarantees while preserving robustness to prediction errors. In caching, existing approaches typically rely on signals such as next-arrival times or ranking scores, whose quality is measured through aggregate or worst-case err...