# Randomized learning-augmented auctions with revenue guarantees

arXiv: https://arxiv.org/abs/2401.13384 · HTML: https://mlsanity.com/paper/2401.13384/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [Learning-Augmented Streaming Algorithms for Correlation Clustering](https://mlsanity.com/paper/2510.10705/) (2025)
- [On multiagent online problems with predictions](https://mlsanity.com/paper/2507.12486/) (2025)

## Abstract (arXiv)

We consider the fundamental problem of designing a truthful single-item auction with the challenging objective of extracting a large fraction of the highest agent valuation as revenue. Following a recent trend in algorithm design, we assume that the agent valuations belong to a known interval, and a (possibly erroneous) prediction for the highest valuation is available. Then, auction design aims for high consistency and robustness, meaning that, for appropriate pairs of values γ and ρ, the extracted revenue should be at least a γ- or ρ-fraction of the highest valuation when the prediction is correct for the input instance or not. We characterize all pairs of parameters γ and ρ so that a randomized γ-consistent and ρ-robust auction exists. Furthermore, for the setting in which robustness can be a function of the prediction error, we give sufficient and necessary conditions for the existence of robust auctions and present randomized auctions that extract a revenue that is only a polylogarithmic (in terms of the prediction error) factor away from the highest agent valuation.