Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Concentration inequalities under sub-Gaussian and sub-exponential conditions
Authors: Andreas Maurer, Massimiliano Pontil
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove analogues of the popular bounded difference inequality (also called Mc Diarmid s inequality) for functions of independent random variables under sub Gaussian and sub-exponential conditions.In this work we use the entropy method ([8], [2], [3]) to extend these inequalities from sums to general functions of independent variables, for which the centered conditional versions are sub-Gaussian or sub-exponential, respectively. These concentration inequalities, Theorem 3, 4 and 5, are stated in Section 3 below. Theorems 4 and 5, which apply to the heavier tailed sub-exponential distributions, are our principal contributions. |
| Researcher Affiliation | Academia | Andreas Maurer Istituto Italiano di Tecnologia EMAIL Massimiliano Pontil Istituto Italiano di Tecnologia & University College London EMAIL |
| Pseudocode | No | The paper focuses on mathematical proofs and theoretical derivations and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code. |
| Open Datasets | No | The paper is theoretical and discusses applications to learning theory problems abstractly, without referring to or using any specific publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper does not describe any experiments involving datasets, and therefore no dataset split information (training, validation, or test) is provided. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experiments requiring hardware specifications. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any experiments that would require specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or configurations. |