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..
Algorithmic Stability and Uniform Generalization
Authors: Ibrahim M. Alabdulmohsin
NeurIPS 2015 | Venue PDF | 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 EMAIL |
| 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. |