# A short note about the learning-augmented secretary problem

arXiv: https://arxiv.org/abs/2410.06583 · HTML: https://mlsanity.com/paper/2410.06583/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

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

- [Fair Secretaries with Unfair Predictions](https://mlsanity.com/paper/2411.09854/) (2024)
- [The Secretary Problem with Predictions and a Chosen Order](https://mlsanity.com/paper/2601.07482/) (2026)

## Abstract (arXiv)

We consider the secretary problem through the lens of learning-augmented algorithms. As it is known that the best possible expected competitive ratio is 1/e in the classic setting without predictions, a natural goal is to design algorithms that are 1-consistent and 1/e-robust. Unfortunately, [FY24] provided hardness constructions showing that such a goal is not attainable when the candidates' true values are allowed to scale with n. Here, we provide a simple and explicit alternative hardness construction showing that such a goal is not achievable even when the candidates' true values are constants that do not scale with n.