# Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location

arXiv: https://arxiv.org/abs/2204.01120 · HTML: https://mlsanity.com/paper/2204.01120/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2022.

## Built on by (most cited first, 19 of 19)

- [Algorithms with Prediction Portfolios](https://mlsanity.com/paper/2210.12438/) (2022)
- [Binary Search with Distributional Predictions](https://mlsanity.com/paper/2411.16030/) (2024)
- [Mechanism Design With Predictions for Obnoxious Facility Location](https://mlsanity.com/paper/2212.09521/) (2022)
- [Learning-Augmented Robust Algorithmic Recourse](https://mlsanity.com/paper/2410.01580/) (2024)
- [Learning multivariate Gaussians with imperfect advice](https://mlsanity.com/paper/2411.12700/) (2024)
- [Approximate Proportionality in Online Fair Division](https://mlsanity.com/paper/2508.03253/) (2025)
- [Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L^natural-Convex Function Minimization](https://mlsanity.com/paper/2302.00928/) (2023)
- [Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget](https://mlsanity.com/paper/2509.00135/) (2025)
- [Learning-Augmented Streaming Algorithms for Correlation Clustering](https://mlsanity.com/paper/2510.10705/) (2025)
- [Product distribution learning with imperfect advice](https://mlsanity.com/paper/2511.10366/) (2025)
- [A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning](https://mlsanity.com/paper/2312.06877/) (2023)
- [MAC Advice for Facility Location Mechanism Design](https://mlsanity.com/paper/2403.12181/) (2024)
- [Prediction-Augmented Mechanism Design for Weighted Facility Location](https://mlsanity.com/paper/2507.06509/) (2025)
- [On multiagent online problems with predictions](https://mlsanity.com/paper/2507.12486/) (2025)
- [Mechanism Design for Facility Location using Predictions](https://mlsanity.com/paper/2508.03818/) (2025)
- [Prediction-Augmented Trees for Reliable Statistical Inference](https://mlsanity.com/paper/2510.16937/) (2025)
- [Impartial Selection with Predictions](https://mlsanity.com/paper/2510.19002/) (2025)
- [Online Packet Scheduling with Deadlines and Learning](https://mlsanity.com/paper/2606.00835/) (2026)
- [Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications](https://mlsanity.com/paper/2609.04787/) (2026)

## Abstract (arXiv)

In this work we introduce an alternative model for the design and analysis of strategyproof mechanisms that is motivated by the recent surge of work in "learning-augmented algorithms". Aiming to complement the traditional approach in computer science, which analyzes the performance of algorithms based on worst-case instances, this line of work has focused on the design and analysis of algorithms that are enhanced with machine-learned predictions regarding the optimal solution. The algorithms can use the predictions as a guide to inform their decisions, and the goal is to achieve much stronger performance guarantees when these predictions are accurate (consistency), while also maintaining near-optimal worst-case guarantees, even if these predictions are very inaccurate (robustness). So far, these results have been limited to algorithms, but in this work we argue that another fertile ground for this framework is in mechanism design. We initiate the design and analysis of strategyproof mechanisms that are augmented with predictions regarding the private information of the participating agents. To exhibit the important benefits of this approach, we revisit the canonical problem of facility location with strategic agents in the two-dimensional Euclidean space. We study both the egalitarian and utilitarian social cost functions, and we propose new strategyproof mechanisms that leverage predictions to guarantee an optimal trade-off between consistency and robustness guarantees. This provides the designer with a menu of mechanism options to choose from, depending on her confidence regarding the prediction accuracy. Furthermore, we also prove parameterized approximation results as a function of the prediction error, showing that our mechanisms perform well even when the predictions are not fully accurate.