Randomized Learning-Augmented Auctions with Revenue Guarantees

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.