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..
A Bias Correction Mechanism for Distributed Asynchronous Optimization
Authors: Yuan Gao, Yuki Takezawa, Sebastian U Stich
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct numerical experiments to corroborate our theoretical findings. ... 5 EXPERIMENTS In this section, we conduct numerical experiments to validate the theoretical results of our algorithms. |
| Researcher Affiliation | Academia | Yuan Gao EMAIL CISPA Helmholtz Center for Information Security Universität des Saarlandes Yuki Takezawa EMAIL Kyoto University OIST Sebastian Stich EMAIL CISPA Helmholtz Center for Information Security |
| Pseudocode | Yes | Algorithm 1 Async BC-GD ... Algorithm 2 Async BC-SGD |
| Open Source Code | Yes | All our code can be accessed at here and here. |
| Open Datasets | Yes | Regularized Logistic Regression For Fashion MNIST Classification Now we consider a regularized logistic regression problem for the opensourced Fashion MNIST dataset (Xiao et al., 2017). |
| Dataset Splits | Yes | We use 20% of the training dataset for the validation dataset. |
| Hardware Specification | Yes | All experiments were run on an Intel(R) Xeon(R) CPU E7-8890 v4 @ 2.20GHz chip. |
| Software Dependencies | No | The paper mentions 'All our code can be accessed at here and here.' but does not specify any software versions for frameworks or libraries used. |
| Experiment Setup | Yes | For the synthetic least squares problem, we set the number of clients n = 4, problem dimension d = 10, and target error (the average of the last 20 iterations) at 0.01. η is searched over {1.0 10 10, 5.0 10 10, , 1.0 10 1}. ... We set the number of clients n = 64, set the batch size to 32, and simulate the different computation speeds as in the previous section, where we set τ = 50. We perform a grid search over {0.5, 0.1, 0.05, 0.01, 0.005} for the best η parameter, and select the step size with the best average accuracy at the last 10 iterations. |