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..
Delay-Adaptive Step-sizes for Asynchronous Learning
Authors: Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian, Mikael Johansson
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a classification problem show that the proposed delay-adaptive step-sizes accelerate the convergence of the two methods compared to the best known fixed step-sizes from the literature. 4. Numerical experiments Although the case for delay-adaptive step-sizes should be clear by now, we also demonstrate the end-effect on a simple machine learning problem. We consider classification problem on the training data sets of RCV1 (Lewis et al., 2004), MNIST (Deng, 2012), and CIFAR100 (Krizhevsky et al., 2009) |
| Researcher Affiliation | Collaboration | 1Division of Decision and Control Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden 2Department of Computer and System Science, Stockholm University, Stockholm, Sweden 3ABB Corporate Research, V aster as, Sweden. |
| Pseudocode | Yes | Algorithm 1 PIAG with delay-tracking ... Algorithm 2 Async-BCD with delay tracking |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We consider classification problem on the training data sets of RCV1 (Lewis et al., 2004), MNIST (Deng, 2012), and CIFAR100 (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper does not explicitly mention using a validation set or provide details about training/validation/test splits. It only states: "We split the samples in each data set into n = 8 batches and assign each batch to a single worker." |
| Hardware Specification | No | The paper states: "We run both PIAG and Async-BCD on a 10-core machine". This is too general and does not provide specific hardware details like CPU model, GPU model, or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We pick (λ1, λ2) = (10 3, 10 4) for all three datasets. ... We compare the two delay-adaptive step-sizes with γ = h L against the fixed step-size γk = h L(τ+1/2) from Sun et al. (2019); Deng et al. (2020), where h = 0.99 for all three step-sizes (larger step-sizes usually lead to faster convergence). |