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..
Random Shuffling Beats SGD after Finite Epochs
Authors: Jeff Haochen, Suvrit Sra
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present the first non-asymptotic results for this problem, proving that after a reasonable number of epochs RANDOMSHUFFLE converges faster than SGD. Specifically, we prove that for strongly convex, second-order smooth functions, the iterates of RANDOMSHUFFLE converge to the optimal solution as O(1/T 2 + n3/T 3) |
| Researcher Affiliation | Academia | 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Massachusetts Institute of Technology. |
| Pseudocode | No | The paper describes the algorithms (SGD and RANDOMSHUFFLE) in text, but does not provide a formal pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not include any statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or the use of datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies. Therefore, no dataset splits for validation are discussed. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications used for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and analysis. It does not mention any specific software dependencies with version numbers required to reproduce experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |