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..
An Asynchronous Bundle Method for Distributed Learning Problems
Authors: Daniel Cederberg, Xuyang Wu, Stephen Boyd, Mikael Johansson
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The practical advantages of our method are illustrated through numerical experiments on classification problems of varying complexities and scales. ... Implementation details and numerical experiments are presented in 5 and 6, respectively. ... We conduct experiments on three datasets (mnist8m/infimnist, epsilon, rcv1) from the LIBSVM library (Chang & Lin, 2011) and on the SVHN dataset (Netzer et al., 2011). |
| Researcher Affiliation | Academia | Daniel Cederberg Stanford University, USA Xuyang Wu SUSTech, China Stephen Boyd Stanford University, USA Mikael Johansson KTH, Sweden |
| Pseudocode | Yes | A summary of the algorithm we propose is given in Algorithm 1. |
| Open Source Code | Yes | The code is written in Python using MPI4PY (Dalcin & Fang, 2021) and is available at https://github.com/dance858/Asynchronous-bundle-method. |
| Open Datasets | Yes | We conduct experiments on three datasets (mnist8m/infimnist, epsilon, rcv1) from the LIBSVM library (Chang & Lin, 2011) and on the SVHN dataset (Netzer et al., 2011). |
| Dataset Splits | No | The data is distributed evenly among the workers. The dataset mnist8m corresponds to a multiclass problem with 10 different labels. To use it for binary classification, we select data corresponding to the digits 7 and 9 and discard the rest. |
| Hardware Specification | No | All methods are evaluated on a workstation using 10 cores. |
| Software Dependencies | No | The code is written in Python using MPI4PY (Dalcin & Fang, 2021) ... To evaluate the objective value and the gradients we use Py Torch (Paszke et al., 2019)... |
| Experiment Setup | Yes | For ABM we use bundle size m = 10, master problem tolerance δ = 10^-7, and adaptive smoothness estimation. DAve-RPG has two hyperparameters: the step size γ and the number of inner prox-steps p. We use step size γ = 1/Laverage where Laverage is the average smoothness parameter of the workers, and p = 1 inner prox-steps (as in Mishchenko et al. (2018)). For PIAG with delay-tracking we implemented the first adaptive step size strategy described in Wu et al. (2022). |