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..
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Authors: Christopher M. De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [N/A] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Researcher Affiliation | Collaboration | Ayush Sekhari EMAIL Satyen Kale EMAIL Jason D. Lee EMAIL Chris De Sa EMAIL Karthik Sridharan EMAIL Google Research, NY Princeton University and Google Research, Princeton Cornell University |
| Pseudocode | No | The paper describes algorithms (Gradient Descent, Stochastic Gradient Descent, Gradient Flow) mathematically, but does not present them in a structured pseudocode block or an explicitly labeled algorithm box. |
| Open Source Code | No | The checklist explicitly states 'N/A' for questions regarding code and datasets, and there is no statement in the paper about releasing source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not report empirical experiments with datasets. The checklist section 'If you ran experiments...' explicitly states '[N/A]' for questions related to data. |
| Dataset Splits | No | The paper does not report empirical experiments or use datasets, therefore, there is no mention of training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not report any empirical experiments, and the checklist section 'If you ran experiments...' explicitly states '[N/A]' for questions related to the total amount of compute and resources used, indicating no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments; therefore, it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report any empirical experiments, thus no experimental setup details, such as hyperparameters or training settings, are provided. |