# Deviation bound for non-causal machine learning

arXiv: https://arxiv.org/abs/2009.08905 · HTML: https://mlsanity.com/paper/2009.08905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

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

- [Concentration inequalities and optimal number of layers for stochastic deep neural networks](https://mlsanity.com/paper/2206.11241/) (2022)

## Abstract (arXiv)

Concentration inequalities are widely used for analyzing machine learning algorithms. However, current concentration inequalities cannot be applied to some of the most popular deep neural networks, notably in natural language processing. This is mostly due to the non-causal nature of such involved data, in the sense that each data point depends on other neighbor data points. In this paper, a framework for modeling non-causal random fields is provided and a Hoeffding-type concentration inequality is obtained for this framework. The proof of this result relies on a local approximation of the non-causal random field by a function of a finite number of i.i.d. random variables.