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..
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
Authors: Chulhee Yun, Shashank Rajput, Suvrit Sra
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Appendix C, we present numerical experiments that corroborate our theoretical findings. |
| Researcher Affiliation | Academia | Chulhee Yun KAIST AI EMAIL Shashank Rajput Univ. of Wisconsin-Madison CS EMAIL Suvrit Sra MIT EECS EMAIL |
| Pseudocode | Yes | Algorithm 1 Local RR (with and without SYNCSHUF) ... Algorithm 2 Minibatch RR (with and without SYNCSHUF) |
| Open Source Code | No | The paper states, 'This paper is a theoretical work, without any experimental results.' (though Appendix C contradicts the experimental claim) and does not provide any link or explicit statement about the release of source code for the described methodology. |
| Open Datasets | No | The paper states it evaluates algorithms 'on the hard instance constructed in our lower bounds (Theorems 3 and 4)'. This refers to a synthetic function constructed for theoretical analysis, not a publicly available dataset with specific access information. It provides parameters like L, µ, ν, N, M rather than dataset names or links. |
| Dataset Splits | No | The paper describes experiments on a synthetic problem instance. It does not mention or define training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware used for running the numerical experiments in Appendix C. It only mentions parameters of the synthetic problem instance (e.g., 'L = 100, µ = 1, ν = 1, N = 768, and M = 16'). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions) used for the numerical experiments. |
| Experiment Setup | Yes | We compare the performance of the algorithms on this problem instance, with L = 100, µ = 1, ν = 1, N = 768, and M = 16, while varying the choice of B {1, 4, 16, 64, 256} and K {1, 3, 5, 7, 10, 30, 50, 70, 100, 300, 500, 700, 1000}. For each value of B and K, we run the algorithms for K epochs (KN/B communication rounds for with-replacement algorithms) starting at x0 = 0 and return the values of F evaluated at the last iterates. |