MAC Advice for Facility Location Mechanism Design

Algorithms with predictions are gaining traction across various domains, as a way to surpass traditional worst-case bounds through (machine-learned) advice. We study the canonical problem of k-facility location mechanism design, where the n agents are strategic and might misreport their locations. We receive a prediction for each agent's location, and these predictions are crucially allowed to be only "mostly" and "approximately" correct (M