Exponential tilting of subweibull distributions
Authors: F. William Townes
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We describe alternative characterizations of subweibull distributions, illustrate their application to concentration inequalities, and detail the conditions under which their tail behavior is preserved after exponential tilting. ... Here, we provide alternative characterizations of the subweibull class and introduce a distinction between strictly and broadly subweibull distributions. ... We demonstrate how subweibull properties can be used to prove Bernstein concentration inequalities in both heavy and light-tailed settings. Finally, we detail the conditions under which the subweibull property is preserved after exponential tilting. |
| Researcher Affiliation | Academia | F. William Townes EMAIL Department of Statistics and Data Science Carnegie Mellon University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It focuses on mathematical definitions, propositions, lemmas, and proofs. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments involving datasets, thus no dataset access information is provided. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments involving datasets, thus no dataset split information is provided. |
| Hardware Specification | No | This paper is theoretical and does not involve experimental evaluation, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not involve experimental evaluation, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This paper is theoretical and does not involve experimental evaluation, thus no experimental setup details or hyperparameters are provided. |