# Set Covering with Our Eyes Wide Shut

arXiv: https://arxiv.org/abs/2304.02063 · HTML: https://mlsanity.com/paper/2304.02063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2023.

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

- [Online Correlation Clustering: Simultaneously Optimizing All ℓp-norms](https://mlsanity.com/paper/2510.15076/) (2025)
- [Adversarial Online Classification with a Preview](https://mlsanity.com/paper/2608.29503/) (2026)

## Abstract (arXiv)

In the stochastic set cover problem (Grandoni et al., FOCS '08), we are given a collection S of m sets over a universe U of size N, and a distribution D over elements of U. The algorithm draws n elements one-by-one from D and must buy a set to cover each element on arrival; the goal is to minimize the total cost of sets bought during this process. A universal algorithm a priori maps each element u ∈ U to a set S(u) such that if U subseteq U is formed by drawing n times from distribution D, then the algorithm commits to outputting S(U). Grandoni et al. gave an O(log mN)-competitive universal algorithm for this stochastic set cover problem. We improve unilaterally upon this result by giving a simple, polynomial time O(log mn)-competitive universal algorithm for the more general prophet version, in which U is formed by drawing from n different distributions D1, ldots, Dn. Furthermore, we show that we do not need full foreknowledge of the distributions: in fact, a single sample from each distribution suffices. We show similar results for the 2-stage prophet setting and for the online-with-a-sample setting. We obtain our results via a generic reduction from the single-sample prophet setting to the random-order setting; this reduction holds for a broad class of minimization problems that includes all covering problems. We take advantage of this framework by giving random-order algorithms for non-metric facility location and set multicover; using our framework, these automatically translate to universal prophet algorithms.