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..
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
Authors: Pan Zhou, Xiaotong Yuan, Jiashi Feng
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical results confirm our theoretical affirmation and demonstrate the favorable efficiency of Wo RS-based HSGD. |
| Researcher Affiliation | Academia | Learning & Vision Lab, National University of Singapore, Singapore B-DAT Lab, Nanjing University of Information Science & Technology, Nanjing, China EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Hybrid SGD under Wo RS |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository. |
| Open Datasets | Yes | All the datasets are public datasets from LibSVM, which can be downloaded from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper does not explicitly specify exact percentages or sample counts for training, validation, and test splits, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments, such as CPU or GPU models, or cloud computing environments with specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, programming language versions) used for the experiments. |
| Experiment Setup | No | While the paper mentions 'Hyper-parameters of all the algorithms are tuned to best' and discusses learning rates and mini-batch size strategies in theory, it does not explicitly provide the specific hyperparameter values or detailed training configurations used in the experiments. |