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..
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Authors: Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct several experiments on real-world datasets which corroborate our theoretical results and conο¬rm the practical superiority of our accelerated methods. |
| Researcher Affiliation | Academia | 1King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia. |
| Pseudocode | Yes | Algorithm 1 Accelerated CGD (ACGD) and Algorithm 2 Accelerated DIANA (ADIANA) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. No mention of code availability in supplementary materials or a repository link was found. |
| Open Datasets | Yes | In our experiments we use four standard datasets, namely, a5a, mushrooms, a9a and w6a from the LIBSVM library. Some of the experiments are provided in the appendix. |
| Dataset Splits | No | The paper mentions using 'standard datasets' from the LIBSVM library but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits within the paper). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications, or cloud instance types) used for running its experiments. It only mentions 'The default number of nodes/machines is 20'. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we use the theoretical stepsize and parameters for all the three algorithms: vanilla distributed compressed gradient descent (DCGD), DIANA (Mishchenko et al., 2019), and our ADIANA (Algorithm 2). The default number of nodes/machines is 20 and the regularization parameter Ξ» = 10β3. |