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 [1].
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. |