# Top-k data selection via distributed sample quantile inference

arXiv: https://arxiv.org/abs/2212.00230 · HTML: https://mlsanity.com/paper/2212.00230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2022.

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

- [Fast networked data selection via distributed smoothed quantile estimation](https://mlsanity.com/paper/2406.01929/) (2024)
- [Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers](https://mlsanity.com/paper/2510.25176/) (2025)

## Abstract (arXiv)

We consider the problem of determining the top-k largest measurements from a dataset distributed among a network of n agents with noisy communication links. We show that this scenario can be cast as a distributed convex optimization problem called sample quantile inference, which we solve using a two-time-scale stochastic approximation algorithm. Herein, we prove the algorithm's convergence in the almost sure sense to an optimal solution. Moreover, our algorithm handles noise and empirically converges to the correct answer within a small number of iterations.