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..
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum
Authors: Hongchang Gao, Junyi Li, Heng Huang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, extensive experimental results demonstrate the superior empirical performance over existing methods, confirming the efficacy of our method. 5. Numerical Experiments |
| Researcher Affiliation | Academia | 1Department of Computer and Information Sciences, Temple University, PA, USA. 2Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA. |
| Pseudocode | Yes | Algorithm 1 Local-SCGDM |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | Yes | We evaluate our proposed algorithm Local SCGDM over a 1-D sinusoid regression problem... and the Few-Shot Classification task over the Omniglot dataset. |
| Dataset Splits | Yes | we construct 25 different training tasks by choosing A = {1, 2, 3, 4, 5} and b = {1, 2, 3, 4, 5} and randomly and evenly distribute them over 5 clients. Then during training, we randomly sample 3 tasks for every client per meta-iteration. For each task we choose K = 10 samples of x [ 5, 5] randomly. We follow the experimental protocols of Vinyals et al. (2016) to divide the alphabets to train/validation/test with 33/5/12, respectively. for each task, we sample K samples for training and 15 samples for validation. |
| Hardware Specification | Yes | All experiments are run over a machine with Intel Xeon Gold 6248 CPU and 4 Nvidia Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions "Pytorch" and "Pytorch.distributed package" but does not specify version numbers for these software components. |
| Experiment Setup | Yes | The inner learning rate is 0.001 for all methods. For other hyper-parameters, we perform grid search for all methods and choose the setting with the best results. More precisely, for Local-BSGD (Local-MAML), we choose meta learning rate 0.01; for Local-SCGD, we choose meta learning rate 0.01 and the inner state momentum coefficient 0.9 (this algorithm diverges with smaller values); for Local-MOML, we choose meta learning rate 0.01, inner state momentum coefficient 0.7; for our Local-SCGDM, we choose η as 1, meta learning rate coefficient β as 0.01, meta momentum coefficient α as 0.8 and inner state momentum coefficient γ as 0.7. We set the number of local epochs as 5 in all comparison experiments. |