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..
MARINA: Faster Non-Convex Distributed Learning with Compression
Authors: Eduard Gorbunov, Konstantin P. Burlachenko, Zhize Li, Peter Richtarik
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several numerical experiments to justify the theoretical claims of the paper. |
| Researcher Affiliation | Collaboration | 1Moscow Institute of Physics and Technology, Moscow, Russia 2Yandex, Moscow, Russia 3King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. |
| Pseudocode | Yes | Algorithm 1 MARINA |
| Open Source Code | Yes | Our code is available at https://github.com/burlachenkok/marina. |
| Open Datasets | Yes | binary classification problem involving non-convex loss (11) with Lib SVM data (Chang & Lin, 2011)... training Res Net-18 (He et al., 2016) at CIFAR100 (Krizhevsky et al., 2009) dataset. |
| Dataset Splits | No | The paper mentions using specific datasets but does not provide explicit train/validation/test splits, percentages, or sample counts for reproducibility. Standard splits might be implied for well-known datasets, but they are not stated. |
| Hardware Specification | No | The paper states 'The distributed environment is simulated' but does not provide any specific hardware details such as GPU/CPU models or memory specifications used for these simulations. |
| Software Dependencies | Yes | The distributed environment is simulated in Python 3.8 using MPI4PY and other standard libraries... The code is wrtitten in Python 3.9 using Py Torch 1.7 |
| Experiment Setup | Yes | Stepsizes for the methods are chosen according to the theory and the batchsizes for VR-MARINA and VR-DIANA are m/100... In all cases, we used the Rand K sparsification operator with K {1, 5, 10}... Number of workers equals 5. Stepsizes for the methods were tuned and the batchsizes are m/50. |