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..
Learning Curves for SGD on Structured Features
Authors: Blake Bordelon, Cengiz Pehlevan
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the accuracy of our theory on random feature models and wide neural networks trained with SGD on real datasets such as MNIST and CIFAR-10. |
| Researcher Affiliation | Academia | Blake Bordelon & Cengiz Pehlevan John A. Paulson School of Engineering and Applied Sciences Center for Brain Science Harvard University Cambridge, MA 02138, USA EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | REPRODUCIBILITY STATEMENT The code to reproduce the experimental components of this paper can be found here https://github.com/Pehlevan-Group/sgd_structured_features, which contains jupyter notebook ο¬les which we ran in Google Colab. |
| Open Datasets | Yes | We demonstrate the accuracy of our theory on random feature models and wide neural networks trained with SGD on real datasets such as MNIST and CIFAR-10. |
| Dataset Splits | No | The paper mentions using 'training points' and 'test set' but does not specify validation splits or other detailed splitting methodology. |
| Hardware Specification | No | The paper mentions that experiments were run in 'Google Colab' but does not provide specific hardware details (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using 'Neural Tangents API' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We explore in detail the effect of minibatch size, m, on learning dynamics. By varying m, we can interpolate our theory between single sample SGD (m = 1) and gradient descent on the population loss (m ). |