# Exponential tilting of subweibull distributions

arXiv: https://arxiv.org/abs/2407.11386 · HTML: https://mlsanity.com/paper/2407.11386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [On Tilted Losses in Machine Learning: Theory and Applications](https://mlsanity.com/paper/2109.06141/) (2021)
- [Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression](https://mlsanity.com/paper/1804.02605/) (2018)
- [Understanding new tasks through the lens of training data via exponential tilting](https://mlsanity.com/paper/2205.13577/) (2022)
- [Nonparametric Estimation of Conditional Incremental Effects](https://mlsanity.com/paper/2212.03578/) (2022)

## Abstract (arXiv)

The class of subweibull distributions has recently been shown to generalize the important properties of subexponential and subgaussian random variables. We describe alternative characterizations of subweibull distributions and detail the conditions under which their tail behavior is preserved after exponential tilting.