Algorithmic Stability and Uniform Generalization
Authors: Ibrahim M. Alabdulmohsin
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we prove that algorithmic stability in the inference process is equivalent to uniform generalization across all parametric loss functions. |
| Researcher Affiliation | Academia | Ibrahim Alabdulmohsin King Abdullah University of Science and Technology Thuwal 23955, Saudi Arabia ibrahim.alabdulmohsin@kaust.edu.sa |
| Pseudocode | No | The paper describes theoretical concepts and mathematical proofs, and does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on mathematical proofs and definitions; it does not describe a software implementation or provide any information regarding open-source code. |
| Open Datasets | No | The paper is theoretical, discussing concepts like 'observation space Z' and 'training set Sm' abstractly, without referring to specific named datasets or providing any access information for them. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, thus no details on dataset splits (training, validation, test) are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or procedures that would require specifying hardware details. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical concepts and proofs, therefore it does not mention any specific software dependencies or versions required for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any practical experiments or their setup, and therefore does not include details such as hyperparameters or training configurations. |