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..
Adaptive Random Walk Gradient Descent for Decentralized Optimization
Authors: Tao Sun, Dongsheng Li, Bao Wang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experimental Results We contrast the performance of adaptive and non-adaptive random walk algorithms for training machine learning models, including logistic regression (LR), multi-layer perceptron (MLP), and convolutional neural networks (CNNs). We evaluate the performance of the models on the benchmark MNIST and CIFAR10 image classification tasks, where MNIST/CIFAR10 contains 60K/50K and 10K/10K images for training and test, respectively. |
| Researcher Affiliation | Academia | 1College of Computer, National University of Defense Technology, Hunan, China. 2Department of Mathematics and Scientific Computing and Imaging Institute, University of Utah. |
| Pseudocode | Yes | Algorithm 1 Adaptive Random Walk Gradient Descent |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the performance of the models on the benchmark MNIST and CIFAR10 image classification tasks, where MNIST/CIFAR10 contains 60K/50K and 10K/10K images for training and test, respectively. |
| Dataset Splits | No | The paper states 'MNIST/CIFAR10 contains 60K/50K and 10K/10K images for training and test, respectively' and 'We randomly partition the training data into ten even groups in an i.i.d. fashion', but it does not specify details about a validation split. |
| Hardware Specification | No | The paper describes the experimental setup in terms of models, datasets, and hyperparameters, but it does not provide specific hardware details such as GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | In training, we set the batch size to be 128. We fine-tune the step size for both adaptive and non-adaptive random walk gradient descent, and we use the initial learning rate of 0.003 and 0.1 for adaptive and non-adaptive algorithms, respectively. The momentum hyperparameter is set to 0.9 for both solvers. Moreover, we set the weight decay for both adaptive and non-adaptive algorithms to be 5 × 10−4. |