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..
Faster Adaptive Decentralized Learning Algorithms
Authors: Feihu Huang, Jianyu Zhao
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct some numerical experiments on training nonconvex machine learning tasks to verify the efficiency of our proposed algorithms. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China 2MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China. |
| Pseudocode | Yes | Algorithm 1 Adaptive Momentum-Based Decentralized Optimization (Ada MDOS) Algorithm for Stochastic Optimization Algorithm 2 Adaptive Momentum-Based Decentralized Optimization (Ada MDOF) Algorithm for Finite-Sum Optimization |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link, explicit code release statement, or code in supplementary materials. |
| Open Datasets | Yes | We use public w8a and covertype datasets1. 1available at https://www.openml.org/ The MNIST dataset (Le Cun et al., 2010) The Tiny-Image Net dataset (Le & Yang, 2015) |
| Dataset Splits | No | The paper specifies training and testing examples/splits for some datasets (e.g., MNIST, Tiny-Image Net) but does not provide explicit details about a validation dataset split or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions a "decentralized network" and "clients" or "nodes" without further hardware specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiment, we set the regularization parameter λ = 10 5, and use the same initial solution x0 = xi 0 = 0.01 ones(d, 1) for all i [m] for all algorithms. In the experiment, for fair comparison, we use the batch size b = 10 in all algorithms, and set β1 = β2 = 0.9 in the DADAM (Nazari et al., 2022) and DAMSGrad (Chen et al., 2023), and set β1 = 0.9 in the DAda Grad (Chen et al., 2023), and set ϱ = βt = ηt = 0.9 for all t 1 in our algorithms. |