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].
SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
Authors: Tehila Dahan, Kfir Y. Levy
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the effectiveness of our proposed approach, we conducted experiments on the MNIST [26] dataset a well-established benchmark in image classification comprising 70,000 grayscale images of handwritten digits (0 9), with 60,000 images designated for training and 10,000 for testing. The dataset was accessed via torchvision (version 0.16.2). We implemented a logistic regression model [7] using the Py Torch framework and executed all computations on an NVIDIA L40S GPU. To ensure robustness, results were averaged over three different random seeds. |
| Researcher Affiliation | Academia | Tehila Dahan Department of Electrical Engineering Technion Haifa, Israel EMAIL Kfir Y. Levy Department of Electrical Engineering Technion Haifa, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 Parallel Stochastic Optimization Template Algorithm 2 SLowcal-SGD |
| Open Source Code | Yes | The complete codebase for these experiments is publicly available on our Git Hub repository.4 4https://github.com/dahan198/slowcal-sgd |
| Open Datasets | Yes | To assess the effectiveness of our proposed approach, we conducted experiments on the MNIST [26] dataset a well-established benchmark in image classification comprising 70,000 grayscale images of handwritten digits (0 9), with 60,000 images designated for training and 10,000 for testing. The dataset was accessed via torchvision (version 0.16.2).... |
| Dataset Splits | Yes | The MNIST [26] dataset a well-established benchmark in image classification comprising 70,000 grayscale images of handwritten digits (0 9), with 60,000 images designated for training and 10,000 for testing. The dataset was accessed via torchvision (version 0.16.2). |
| Hardware Specification | Yes | We implemented a logistic regression model [7] using the Py Torch framework and executed all computations on an NVIDIA L40S GPU. |
| Software Dependencies | Yes | The dataset was accessed via torchvision (version 0.16.2). We implemented a logistic regression model [7] using the Py Torch framework and executed all computations on an NVIDIA L40S GPU. |
| Experiment Setup | Yes | For fairness, the learning rate was selected through grid search, with a value of 0.01 for SLowcal-SGD and Local-SGD, and 0.1 for Minibatch-SGD. More details about the data distribution across workers and complete experimental results are provided in Appendix M. |