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..
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Authors: Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate the theory with experimental results, running over AWS EC2 clouds and an internal GPU cluster. |
| Researcher Affiliation | Academia | Farzin Haddadpour Penn State EMAIL Mohammad Mahdi Kamani Penn State EMAIL Mehrdad Mahdavi Penn State EMAIL Viveck R. Cadambe Penn State EMAIL |
| Pseudocode | Yes | Algorithm 1 LUPA-SGD(τ): Local updates with periodic averaging. |
| Open Source Code | Yes | The implementation code is available at https://github.com/mmkamani7/LUPA-SGD. |
| Open Datasets | Yes | Epsilon dataset2, a popular large scale binary dataset, consists of 400, 000 training samples and 100, 000 test samples with feature dimension of 2000. (Footnote 2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) |
| Dataset Splits | No | The paper mentions 400,000 training samples and 100,000 test samples but does not specify a validation split or details on how data was partitioned for validation. |
| Hardware Specification | Yes | Most of the experiments will be run on Amazon EC2 cluster with 5 p2.xlarge instances. ... We also use an internal high performance computing (HPC) cluster equipped with NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions software like PyTorch, TensorFlow, mpi4py, and Open MPI, but it does not specify version numbers for any of these dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | The learning rate and regularization parameter are 0.01 and 1 10 4, respectively, and the minibatch size is 128 unless otherwise stated. |