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..
COLA: Decentralized Linear Learning
Authors: Lie He, An Bian, Martin Jaggi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we illustrate the advantages of COLA in three respects: firstly we investigate the application in different network topologies and with varying subproblem quality ; secondly, we compare COLA with state-of-the-art decentralized baselines: 1 , DIGing [Nedic et al., 2017], which generalizes the gradient-tracking technique of the EXTRA algorithm [Shi et al., 2015], and 2 , Decentralized ADMM (aka. consensus ADMM), which extends the classical ADMM (Alternating Direction Method of Multipliers) method [Boyd et al., 2011] to the decentralized setting [Shi et al., 2014, Wei and Ozdaglar, 2013]; Finally, we show that COLA works in the challenging unreliable network environment where each node has a certain chance to drop out of the network. |
| Researcher Affiliation | Academia | EPFL EMAIL An Bian ETH Zurich EMAIL Martin Jaggi EPFL EMAIL |
| Pseudocode | Yes | Algorithm 1: COLA: Communication-Efficient Decentralized Linear Learning |
| Open Source Code | Yes | Our implementation is publicly available under github.com/epfml/cola. |
| Open Datasets | Yes | Table 1: Datasets Used for Empirical Study URL 2M 3M 3.5e-5 Webspam 350K 16M 2.0e-4 Epsilon 400K 2K 1.0 RCV1 Binary 677K 47K 1.6e-3 |
| Dataset Splits | No | No specific details about train/validation/test splits are provided in the main text. The order of columns is mentioned to be shuffled before distribution, but no split ratios. |
| Hardware Specification | Yes | The decentralized network topology is simulated by running one thread per graph node, on a 2 12 core Intel Xeon CPU E5-2680 v3 server with 256 GB RAM. |
| Software Dependencies | No | We implement all algorithms in Py Torch with MPI backend. No specific version numbers for PyTorch or MPI are provided. |
| Experiment Setup | No | Due to space limit, details on the experimental configurations are included in Appendix D. These details are not present in the provided main text. |