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..
Decentralized Accelerated Proximal Gradient Descent
Authors: Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study shows that the proposed algorithm outperforms existing state-of-the-art algorithms. |
| Researcher Affiliation | Academia | 1Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen 2Department of Mathematics, The Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 DPAG; Algorithm 2 Fast Mix |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper mentions using 'datasets w8a and w9a which can be downloaded in libsvm datasets.' but does not provide a specific link, DOI, or formal citation with authors/year for public access. |
| Dataset Splits | No | The paper does not provide specific dataset split information, only mentioning the total number of agents and data points. |
| Hardware Specification | No | The paper does not specify any hardware details like GPU/CPU models or types of machines used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Experiments Setting In our experiments, we consider random networks where each pair of agents have a connection with a probability of p = 0.1. We set W = I L λ1(L) where L is the Laplacian matrix associated with a weighted graph, and λ1(L) is the largest eigenvalue of L. We set m = 100, that is, there exists 100 agents in this network. In our experiments, the gossip matrix W satisfies 1 λ2(W) = 0.05. ... We set σ1 = 10 4 for all datasets and set σ2 as 10 3, 10 4 and 10 5 to control the condition number of the objective function. ... In the experiments, we set K = 1, 2, 3 respectively. |