# Skyline Computation with Noisy Comparisons

arXiv: https://arxiv.org/abs/1710.02058 · HTML: https://mlsanity.com/paper/1710.02058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

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

- [How to Design Robust Algorithms using Noisy Comparison Oracle](https://mlsanity.com/paper/2105.05782/) (2021)
- [Metric k-clustering using only Weak Comparison Oracles](https://mlsanity.com/paper/2601.19333/) (2026)

## Abstract (arXiv)

Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We adopt the noisy comparison model [FRPU94] where comparisons fail with constant probability and confidence can be increased through independent repetitions of a comparison. In this model motivated by Crowdsourcing applications, Groz & Milo [GM15] show three bounds on the query complexity for the skyline problem. We improve significantly on that state of the art and provide two output-sensitive algorithms computing the skyline with respective query complexity O(ndlog (dk/δ)) and O(ndklog (k/δ)) where k is the size of the skyline and δ the expected probability that our algorithm fails to return the correct answer. These results are tight for low dimensions.