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..
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization
Authors: Rafał Szlendak, Alexander Tyurin, Peter Richtárik
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical results with carefully engineered synthetic experiments with minimizing the average of nonconvex quadratics, and on autoencoder training with the MNIST dataset. |
| Researcher Affiliation | Academia | Rafał Szlendak KAUST Saudi Arabia Alexander Tyurin KAUST Saudi Arabia Peter Richtárik KAUST Saudi Arabia |
| Pseudocode | Yes | Algorithm 1 MARINA |
| Open Source Code | No | The paper discusses implementation details (Appendix J) but does not provide a specific repository link or an explicit statement about releasing the source code for their methodology. |
| Open Datasets | Yes | training with the MNIST dataset. (Le Cun et al., 2010) |
| Dataset Splits | Yes | Initially, we randomly split MNIST into n + 1 parts: D0, D1, , Dn, where n = 1000 is the number of nodes. Then, for all i {1, . . . , n}, the ith node takes split D0 with probability bp, or split Di with probability 1 bp. We define the chosen split as c Di. |
| Hardware Specification | Yes | All methods are implemented in Python 3.6 and run on a machine with 24 Intel(R) Xeon(R) Gold 6146 CPU @ 3.20GHz cores with 32-bit precision. |
| Software Dependencies | No | All methods are implemented in Python 3.6. |
| Experiment Setup | Yes | We take MARINA s and EF21 s parameters prescribed by the theory and performed a grid search for the step sizes for each compressor by multiplying the theoretical ones with powers of two. We fix λ = 1e-6, and dimension d = 1000 (see Figure 1). We then generated optimization tasks with the number of nodes n {10, 1000, 10000} and L {0, 0.05, 0.1, 0.21, 0.91}. |