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 [1].
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. |