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..
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Authors: Yunwen Lei, Yiming Ying
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a fine-grained analysis of stability and generalization for SGD by substantially relaxing these assumptions. Firstly, we establish stability and generalization for SGD by removing the existing bounded gradient assumptions. |
| Researcher Affiliation | Academia | Yunwen Lei 1 2 Yiming Ying 3 1Department of Computer Science, University of Kaiserslautern, Germany 2School of Computer Science, University of Birmingham, United Kingdom 3Department of Mathematics and Statistics, State University of New York at Albany, USA. |
| Pseudocode | No | The paper describes the SGD update rule mathematically (Definition 2) but does not provide a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | No | The paper discusses 'training examples' theoretically but does not reference or provide access information for any specific, publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments with specific datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, therefore no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, therefore no experimental setup details are provided. |