AG 1   Info

AG Audience

English

30 Minutes

Saarbrücken

In the strategic facility location problem, a set of agents report their locations in a metric space, and the goal is to use these reports to open a new facility, minimizing an aggregate distance measure from the agents to the facility. However, agents are strategic and may misreport their locations to influence the facility's placement in their favor. The aim is to design truthful mechanisms that ensure agents cannot gain by misreporting. This problem was recently revisited through the learning-augmented framework, aiming to move beyond worst-case analysis by designing truthful mechanisms augmented with (machine-learned) predictions.

In this talk, we first introduce Learning-Augmented Algorithms and then explore the Strategic Facility Location problem in randomized settings, studying the impact of different types of predictions on the performance of truthful learning-augmented mechanisms.

Nidhi Rathi

+49 681 9325 1134

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