# Distributed Black-Box Optimization via Error Correcting Codes

arXiv: https://arxiv.org/abs/1907.05984 · HTML: https://mlsanity.com/paper/1907.05984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Randomized Polar Codes for Anytime Distributed Machine Learning](https://mlsanity.com/paper/2309.00682/) (2023)

## Abstract (arXiv)

We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers. The proposed method employs coded search directions at which the objective function is evaluated, and a decoding step to find the next iterate. Our framework can be seen as an extension of evolution strategies and structured exploration methods where structured search directions were utilized. As an application, we consider black-box adversarial attacks on deep convolutional neural networks. Our numerical experiments demonstrate a significant improvement in the computation times.