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..
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
Authors: Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide the stability and generalization analysis of stochastic compositional gradient descent algorithms in the framework of statistical learning theory. |
| Researcher Affiliation | Academia | 1Department of Mathematics and Statistics, State University of New York at Albany, Albany, NY 12222, USA 2Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA 3The University of Sydney, School of Mathematics and Statistics, Sydney, NSW 2006, Australia. |
| Pseudocode | Yes | Algorithm 1 (Stochastically Corrected) Stochastic Compositional Gradient Descent |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on analysis rather than experimental evaluation, so it does not describe using specific publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setup details such as training, validation, or test data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies or version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations. |