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..
Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
Authors: Tongle Wu, Ying Sun
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results are provided to validate the effectiveness of DGD and T-DGD for sparse learning through implicit regularization.This section conducts the experimental studies to evaluate the theoretical findings of DGD and T-DGD for solving problem (2) in Subsection 6.1, Subsection 6.2, respectively. |
| Researcher Affiliation | Academia | Tongle Wu The Pennsylvania State University EMAIL Ying Sun The Pennsylvania State University EMAIL |
| Pseudocode | No | The paper describes algorithms through mathematical equations and textual explanations but does not include explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use vanilla decentralized SGD(DSGD) to train a depth-2, 5000 hidden Re LU network with the cross-entropy loss on the MNIST dataset...on CIFAR10. |
| Dataset Splits | Yes | 60000 total training samples and 10000 test samples are uniformly allocated to agents.The step sizes were optimally tuned for each α individually to achieve the best validation error. |
| Hardware Specification | Yes | All experiments are conducted on 12th Gen Intel(R) Core(TM) i7-12700@2.10GHz processor and 16.0GB RAM under Windows 11 system. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments (e.g., specific Python or PyTorch versions). |
| Experiment Setup | Yes | We set d = 2000, s = 10, m = 10, N = 400, ρ = 0.1778, α = 10 6.We select the maximum initialization α that achieves optimal statistical error, resulting in α = 10 8 for d = 4 102, α = 10 8.5 for d = 4 103 and α = 10 9 for d = 4 104.Each agent uses the same batch size 256 to train in DSGD.small step size 10 4 for 2000 epochs. |