# Some compact notations for concentration inequalities and user-friendly results

arXiv: https://arxiv.org/abs/1912.13463 · HTML: https://mlsanity.com/paper/1912.13463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Clustering a Mixture of Gaussians with Unknown Covariance](https://mlsanity.com/paper/2110.01602/) (2021)
- [Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation](https://mlsanity.com/paper/2403.19720/) (2024)
- [Consistency of Lloyd's Algorithm Under Perturbations](https://mlsanity.com/paper/2309.00578/) (2023)

## Abstract (arXiv)

This paper presents compact notations for concentration inequalities and convenient results to streamline probabilistic analysis. The new expressions describe the typical sizes and tails of random variables, allowing for simple operations without heavy use of inessential constants. They bridge classical asymptotic notations and modern non-asymptotic tail bounds together. Examples of different kinds demonstrate their efficacy.